Regression_lasso.ipynb

{
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    "# 连续变量预测：线性回归\n",
    "\n",
    "**回归**（**regression**）是统计学中最常用的**拟合**（**fitting**）和**预测**（**predicting**）方法。针对不同的数据通常有不同的回归方法，比如针对连续变量预测的线性回归、分位数回归，以及针对离散变量的Logistic回归等。\n",
    "\n",
    "通常回归方法对特征的函数假定非常强烈，因而在以上**参数**（**parametric**）回归基础上，还有对函数形式假设更加宽松的**非参数**（**nonparametric**）回归。\n",
    "\n",
    "这里我们首先介绍参数回归中最简单的一个类型：针对连续的预测目标（被解释变量）的线性回归。"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "## 线性回归\n",
    "\n",
    "针对连续的预测目标，最常用、最简单的方法是使用普通线性回归。\n",
    "\n",
    "我们不不妨先从总体的角度考虑预测问题。如果我们有连续变量$y$以及一些特征$x$，我们的目的是使用特征$x$对$y$进行预测，意味着我们需要使用特征$x$的一个函数：$h\\left(x\\right)$对$y$进行预测，那么预测误差为：$$y-h\\left(x\\right)$$误差必然越‘小’越好，但是有两个困难阻碍了我们定义‘小’：\n",
    "\n",
    "* 误差可正可负\n",
    "* 误差是随机的\n",
    "\n",
    "为了克服以上第一个困难，我们可以把误差求平方，从而误差的平方都为正数，且只有当误差为0时，误差的平方也为0，达到了完美预测，因而预测误差的平方应该是越小越好。\n",
    "\n",
    "为了克服第二个困难，我们可以对误差平方求期望。如此，就诞生了**均方误差**（**mean squared error，MSE**）：$$\\mathbb{E}\\left[\\left(y-h\\left(x\\right)\\right)^{2}\\right]$$均方误差越小说明预测误差越小，当均方误差为0时，模型达到了完全拟合。当然完全拟合或者完美预测在现实中是不可能达到的，所以均方误差总是大于0的。\n",
    "\n",
    "如果一个函数$h^*\\left(x\\right)$能够使得以上均方误差最小：$$h^*\\left(x\\right)=\\arg\\min_{h\\in\\mathbb{H}}\\left\\{ \\mathbb{E}\\left[\\left(y-h\\left(x\\right)\\right)^{2}\\right]\\right\\} $$那么我们称$h^*\\left(x\\right)$为$y$给定$x$的**条件期望**（**conditional expectation**），并记为：$\\mathbb{E}\\left(y|x\\right)$。\n",
    "\n",
    "然而$h\\left(x\\right)$的可能性太多，为了简化问题，我们必须对$h\\left(x\\right)$做一定的假设。其中最强的一个假设是假设$h\\left(x\\right)$为$x$的线性函数，即：$$h\\left(x\\right)=\\beta_1\\times x_1+\\beta_2\\times x_2+...+\\beta_K\\times x_K=x'\\beta$$其中$$x=\\left[\\begin{array}{c}\n",
    "x_{1}\\\\\n",
    "x_{2}\\\\\n",
    "\\vdots\\\\\n",
    "x_{K}\n",
    "\\end{array}\\right],\\beta=\\left[\\begin{array}{c}\n",
    "\\beta_{1}\\\\\n",
    "\\beta_{2}\\\\\n",
    "\\vdots\\\\\n",
    "\\beta_{K}\n",
    "\\end{array}\\right]$$不是一般性，我们通常会假设第一个变量$x_1=1$，从而$\\beta_1$为**常数项**（**constant term**）。如此，我们将寻找函数$h^*\\left(x\\right)$的问题转换为了寻找参数$\\beta_0$的问题：$$\\mathbb{E}\\left(y|x\\right)=x'\\beta_{0}=\\arg\\min_{\\beta}\\left\\{ \\mathbb{E}\\left[\\left(y-x'\\beta\\right)^{2}\\right]\\right\\} $$只要$\\beta_0$确定了，那么$h^*\\left(x\\right)$就确定了。\n",
    "\n",
    "以上是总体层面的讨论。如果考虑样本层面，我们需要使用数据对$\\beta_0$进行估计，那么我们可以将以上的期望符号换位平均符号，即：$$\\hat{\\beta}=\\arg\\min_{\\beta}\\frac{1}{N}\\sum_{i=1}^{N}\\left(y_{i}-x_{i}'\\beta\\right)^{2}$$以上即经典的**普通最小二乘法**（**ordinary least squares**）。如果我们记：$$Y=\\left[\\begin{array}{c}\n",
    "y_{1}\\\\\n",
    "y_{2}\\\\\n",
    "\\vdots\\\\\n",
    "y_{N}\n",
    "\\end{array}\\right],X=\\left[x_{1},x_{2},\\cdots x_{N}\\right]'=\\left[\\begin{array}{cccc}\n",
    "x_{11} & x_{12} & \\cdots & x_{1K}\\\\\n",
    "x_{21} & x_{22} & \\cdots & x_{2K}\\\\\n",
    "\\vdots & \\vdots & \\ddots & \\vdots\\\\\n",
    "x_{N1} & x_{N2} & \\cdots & x_{NK}\n",
    "\\end{array}\\right]$$其中：$$x_i=\\left[\\begin{array}{c}\n",
    "x_{i1}\\\\\n",
    "x_{i2}\\\\\n",
    "\\vdots\\\\\n",
    "x_{iK}\n",
    "\\end{array}\\right]$$那么最小二乘法的解可以写为：$$\\hat{b}=\\left(X'X\\right)^{-1}X'Y=\\left[\\sum_{i=1}^{N}\\left(x_{i}x_{i}'\\right)\\right]^{-1}\\left[\\sum_{i=1}^{N}\\left(x_{i}y_{i}\\right)\\right]$$"
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   "cell_type": "code",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pid</th>\n",
       "      <th>fid14</th>\n",
       "      <th>provcd14</th>\n",
       "      <th>cfps_birthy</th>\n",
       "      <th>cfps_minzu</th>\n",
       "      <th>cfps_party</th>\n",
       "      <th>cfps_gender</th>\n",
       "      <th>te4</th>\n",
       "      <th>qa301</th>\n",
       "      <th>qa302</th>\n",
       "      <th>...</th>\n",
       "      <th>qq403b</th>\n",
       "      <th>qq1001</th>\n",
       "      <th>qq1101</th>\n",
       "      <th>qq1102</th>\n",
       "      <th>pn401a</th>\n",
       "      <th>pw1r</th>\n",
       "      <th>pg01</th>\n",
       "      <th>pg02</th>\n",
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       "      <th>0</th>\n",
       "      <td>100051501</td>\n",
       "      <td>100051</td>\n",
       "      <td>11</td>\n",
       "      <td>1969</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100051502</td>\n",
       "      <td>100051</td>\n",
       "      <td>11</td>\n",
       "      <td>1966</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>30.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100453431</td>\n",
       "      <td>100453</td>\n",
       "      <td>43</td>\n",
       "      <td>1981</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>101129501</td>\n",
       "      <td>101129</td>\n",
       "      <td>13</td>\n",
       "      <td>1990</td>\n",
       "      <td>-8</td>\n",
       "      <td>-8</td>\n",
       "      <td>0</td>\n",
       "      <td>-8</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>...</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>103671501</td>\n",
       "      <td>103671</td>\n",
       "      <td>21</td>\n",
       "      <td>1988</td>\n",
       "      <td>-8</td>\n",
       "      <td>-8</td>\n",
       "      <td>0</td>\n",
       "      <td>-8</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "</table>\n",
       "<p>5 rows × 90 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         pid   fid14  provcd14  cfps_birthy  cfps_minzu  cfps_party  \\\n",
       "0  100051501  100051        11         1969          -8          -8   \n",
       "1  100051502  100051        11         1966          -8          -8   \n",
       "2  100453431  100453        43         1981           0           0   \n",
       "3  101129501  101129        13         1990          -8          -8   \n",
       "4  103671501  103671        21         1988          -8          -8   \n",
       "\n",
       "   cfps_gender  te4  qa301  qa302  ...  qq403b  qq1001  qq1101  qq1102  \\\n",
       "0            0   -8    3.0    1.0  ...    10.0     2.0     1.0     3.0   \n",
       "1            1   -8    3.0    1.0  ...    30.0     5.0     1.0     1.0   \n",
       "2            1   -8    1.0    4.0  ...     0.0     1.0     1.0     2.0   \n",
       "3            0   -8    1.0    3.0  ...     0.0     2.0     0.0    -8.0   \n",
       "4            0   -8    3.0    1.0  ...     0.0     7.0     0.0    -8.0   \n",
       "\n",
       "   pn401a  pw1r  pg01  pg02  pgc103a  pg501  \n",
       "0     NaN   NaN   NaN   NaN      NaN    NaN  \n",
       "1     NaN   NaN   NaN   NaN      NaN    NaN  \n",
       "2     NaN   NaN   NaN   NaN      NaN    NaN  \n",
       "3     NaN   NaN   NaN   NaN      NaN    NaN  \n",
       "4     NaN   NaN   NaN   NaN      NaN    NaN  \n",
       "\n",
       "[5 rows x 90 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "raw_data = pd.read_csv('csv/cfps_adult.csv')\n",
    "raw_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>qp102</th>\n",
       "      <th>qp101</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>190.0</td>\n",
       "      <td>164.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>140.0</td>\n",
       "      <td>169.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>130.0</td>\n",
       "      <td>180.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>89.0</td>\n",
       "      <td>155.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100.0</td>\n",
       "      <td>160.0</td>\n",
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      ],
      "text/plain": [
       "   qp102  qp101\n",
       "0  190.0  164.0\n",
       "1  140.0  169.0\n",
       "2  130.0  180.0\n",
       "3   89.0  155.0\n",
       "4  100.0  160.0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 挑选被解释变量和解释变量\n",
    "subdata = raw_data.loc[:, ['qp102', 'qp101']]\n",
    "subdata = subdata.dropna()\n",
    "subdata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>qp102</th>\n",
       "      <th>qp101</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>33476.000000</td>\n",
       "      <td>33476.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>121.267911</td>\n",
       "      <td>160.219590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>24.987053</td>\n",
       "      <td>26.447926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-8.000000</td>\n",
       "      <td>-9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>106.000000</td>\n",
       "      <td>158.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>120.000000</td>\n",
       "      <td>164.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>136.000000</td>\n",
       "      <td>170.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>260.000000</td>\n",
       "      <td>216.000000</td>\n",
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      ],
      "text/plain": [
       "              qp102         qp101\n",
       "count  33476.000000  33476.000000\n",
       "mean     121.267911    160.219590\n",
       "std       24.987053     26.447926\n",
       "min       -8.000000     -9.000000\n",
       "25%      106.000000    158.000000\n",
       "50%      120.000000    164.000000\n",
       "75%      136.000000    170.000000\n",
       "max      260.000000    216.000000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subdata.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>qp102</th>\n",
       "      <th>qp101</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>32491.000000</td>\n",
       "      <td>32491.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>122.582764</td>\n",
       "      <td>164.221846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>22.481906</td>\n",
       "      <td>8.054319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>130.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>108.000000</td>\n",
       "      <td>160.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>120.000000</td>\n",
       "      <td>165.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>137.000000</td>\n",
       "      <td>170.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>260.000000</td>\n",
       "      <td>216.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              qp102         qp101\n",
       "count  32491.000000  32491.000000\n",
       "mean     122.582764    164.221846\n",
       "std       22.481906      8.054319\n",
       "min       50.000000    130.000000\n",
       "25%      108.000000    160.000000\n",
       "50%      120.000000    165.000000\n",
       "75%      137.000000    170.000000\n",
       "max      260.000000    216.000000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subdata = subdata[(subdata['qp101'] >= 130) & (subdata['qp102'] > 0)]\n",
    "subdata.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "系数= [1.58369352]\n",
      "截距项= -137.49430839521733\n"
     ]
    }
   ],
   "source": [
    "y = subdata.iloc[:, 0]\n",
    "X = subdata.iloc[:, 1:]\n",
    "## 创建模型对象\n",
    "from sklearn import linear_model\n",
    "\n",
    "reg = linear_model.LinearRegression()\n",
    "## 进行回归\n",
    "reg.fit(X, y)  ## 训练模型\n",
    "## 查看系数\n",
    "print(\"系数=\", reg.coef_)\n",
    "print('截距项=', reg.intercept_)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接下来可以进行预测：$$\\hat{y}=x'\\hat{\\beta}$$，比如如果我们知道某个人身高为175cm，而不知道其具体身高，那么对其身高的最优预测为：$$\\hat{y}_{175}=-137.4943+1.58369\\times175=139.652$$使用scikit-learn做预测："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/Anaconda/lib/python3.11/site-packages/sklearn/base.py:493: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([131.73358941, 139.65205699, 147.57052457])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "reg.predict(np.array([[170], [175], [180]]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "以及对所有样本进行样本内预测："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([122.23142831, 130.14989589, 147.57052457, ..., 126.98250886,\n",
       "       147.57052457, 122.23142831])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yhat = reg.predict(X)\n",
    "yhat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "当然我们可以包括更多的解释变量，比如："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "系数= [ 0.23227872 16.08926018]\n",
      "截距项= 76.06295134743743\n"
     ]
    }
   ],
   "source": [
    "subdata = raw_data.loc[:, ['qp102', 'qp101', 'cfps_gender']]\n",
    "subdata = subdata.dropna()\n",
    "y = subdata.iloc[:, 0]\n",
    "X = subdata.iloc[:, 1:]\n",
    "## 创建模型对象\n",
    "reg = linear_model.LinearRegression()\n",
    "## 进行回归\n",
    "reg.fit(X, y)  ## 训练模型\n",
    "## 查看系数\n",
    "print(\"系数=\", reg.coef_)\n",
    "print('截距项=', reg.intercept_)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果需要预测，同样可以使用predict()方法："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/Anaconda/lib/python3.11/site-packages/sklearn/base.py:493: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([115.55033371, 132.80098749, 133.96238109])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.predict(np.array([[170, 0], [175, 1], [180, 1]]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "以及包含平方项、交叉项等等："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "系数= [-1.03361111 -0.93091655  0.00736492  0.03281556]\n",
      "截距项= 90.39354698294906\n"
     ]
    }
   ],
   "source": [
    "subdata = raw_data.loc[:, ['qp102', 'qp101', 'cfps_gender']]\n",
    "subdata = subdata.dropna()\n",
    "subdata['qp101_2'] = subdata['qp101']**2\n",
    "subdata['qp101_gender'] = subdata['qp101'] * subdata['cfps_gender']\n",
    "y = subdata.iloc[:, 0]\n",
    "X = subdata.iloc[:, 1:]\n",
    "## 创建模型对象\n",
    "reg = linear_model.LinearRegression()\n",
    "## 进行回归\n",
    "reg.fit(X, y)  ## 训练模型\n",
    "## 查看系数\n",
    "print(\"系数=\", reg.coef_)\n",
    "print('截距项=', reg.intercept_)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "以及样本内预测："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([118.9682252 , 130.67767589, 147.94285447, ..., 127.73004044,\n",
       "       147.94285447, 118.9682252 ])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.predict(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 函数形式的讨论\n",
    "\n",
    "虽然线性回归方法非常简单，但是线性函数的假设太强，很多时候我们需要详细讨论函数形式的问题。在有的情况下，函数形式可能会非常有问题，在使用时需要仔细讨论。比如以下例子：\n",
    "\n",
    "* （支撑集问题）支撑集（support）即一个随机变量的取值范围。如果被解释变量为家庭的储蓄率（saving_rate），我们知道储蓄率的取值范围应该在0到1之间。此时，如果我们选取家庭资产规模（wealth）作为解释变量：$$saving\\_rate_{i}=\\beta_{0}+\\beta_{1}\\cdot wealth_{i}+u_{i}$$由于家庭资产规模的取值范围应为$wealth_{i}\\in[0,\\infty)$，因而不管回归得到的系数$\\hat{\\beta}_{1}$是正或者负，对于一个资产规模足够大的家庭，总会使得预测的储蓄率超过1（或者低于0）。\n",
    "* （理论模型的函数形式）在国际贸易理论中（Head and Mayer, 2014），双边贸易与两个国家的GDP之间存在着被称为“引力模型”的关系，即：$$X_{ni}=GY_{i}^{a}Y_{n}^{b}\\phi_{ni}$$其中下标$i$代表国家，而$n$代表出口目的地国，$X_{ni}$为两国之间的贸易额，$G$为常数，$Y$为国家的GDP，$\\phi_{ni}$则是两国之间贸易成本的函数。双边贸易额与GDP之间的关系并非简单的线性关系。\n",
    "\n",
    "解决函数形式的一些方法：\n",
    "\n",
    "解决方案：\n",
    "\n",
    "* 使用非参数回归（kernel, sieve）\n",
    "* 引入多项式（平方项、交叉项）\n",
    "* 对数据进行变换：\n",
    "  - 对数变换：将正数变换到$\\left(-\\infty,\\infty\\right)$上——具有弹性解释\n",
    "  - Box-Cox变换：$$y\\left(\\lambda\\right)=\\begin{cases}\n",
    "\\frac{y^{\\lambda}-1}{\\lambda} & \\lambda\\neq0\\\\\n",
    "\\ln y & \\lambda=0\n",
    "\\end{cases}$$\n",
    "  - Logistic逆变换：使用$$f\\left(x\\right)=\\ln\\frac{x}{1-x}$$将$\\left(0,1\\right)$区间上的实数映射到$\\left(-\\infty,\\infty\\right)$上"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "系数= [1.74333395 0.04237759]\n",
      "截距项= -4.119634539552865\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/Anaconda/lib/python3.11/site-packages/pandas/core/arraylike.py:399: RuntimeWarning: invalid value encountered in log\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "log_subdata = raw_data.loc[:, ['qp102', 'qp101', 'cfps_gender']]\n",
    "log_subdata['qp102'] = np.log(log_subdata['qp102'])\n",
    "log_subdata['qp101'] = np.log(log_subdata['qp101'])\n",
    "log_subdata = log_subdata.dropna()\n",
    "y = log_subdata.iloc[:, 0]\n",
    "X = log_subdata.iloc[:, 1:]\n",
    "reg.fit(X, y)  ## 训练模型\n",
    "## 查看系数\n",
    "print(\"系数=\", reg.coef_)\n",
    "print('截距项=', reg.intercept_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.77113575, 4.86586964, 4.97580103, ..., 4.84511542, 4.97580103,\n",
       "       4.77113575])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.predict(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 模型评价方法\n",
    "\n",
    "通常选择不同的模型是在「大模型」和「小模型」之间进行选择。小模型更容易欠拟合，而大模型更容易过拟合，评价模型主要是为了使模型能够恰好拟合，达到最优的预测效果。\n",
    "\n",
    "在线性回归中，我们通常会使用$R^2$作为拟合程度的一个度量，即$$R^2=1-\\frac{\\hat{e}'\\hat{e}}{Y'M_{0}Y}=1-\\frac{RSS}{TSS}$$其中RSS为残差平方和，TSS为总平方和。然而$R^2$一般不能作为模型选择的标准，过高的$R^2$可能是过拟合的体现。为此针对线性回归，至少有这些标准可以使用：\n",
    "\n",
    "* 调整的$R^2$，即：$$\\bar{R}^{2}=1-\\frac{\\hat{e}'\\hat{e}/\\left(N-K\\right)}{Y'M_{0}Y/\\left(N-1\\right)}=1-\\frac{N-1}{N-K}\\left(1-R^{2}\\right)$$\n",
    "* 赤池信息准则（Akaike information creterion,AIC）：$$AIC=-2Log\\_Likelihood+2K$$\n",
    "* 贝叶斯信息准则（Bayesian information creterion,BIC，又称施瓦茨信息准则，Schwarz information creterion,SIC）：$$BIC=-2Log\\_Likelihood+\\ln\\left(N\\right)K$$\n",
    "\n",
    "其中AIC、BIC适用于所有使用极大似然估计的统计模型。\n",
    "\n",
    "以上的准则都是在拟合程度的度量基础上，对变量个数进行惩罚，避免选出比较「大」的模型。\n",
    "\n",
    "比如我们使用如下代码计算以上指标，首先计算回归："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "系数= [0.31582403] 截距项= 70.66671440178149\n",
      "系数= [ 0.23227872 16.08926018] 截距项= 76.06295134743743\n",
      "系数= [-1.04245345  4.27682516  0.00746794] 截距项= 89.23758538186534\n",
      "系数= [-1.03361111 -0.93091655  0.00736492  0.03281556] 截距项= 90.39354698294906\n"
     ]
    }
   ],
   "source": [
    "subdata = raw_data.loc[:, ['qp102', 'qp101', 'cfps_gender']]\n",
    "subdata = subdata.dropna()\n",
    "subdata['qp101_2'] = subdata['qp101']**2\n",
    "subdata['qp101_gender'] = subdata['qp101'] * subdata['cfps_gender']\n",
    "y = subdata.iloc[:, 0]\n",
    "X1 = subdata.iloc[:, 1:2]\n",
    "X2 = subdata.iloc[:, 1:3]\n",
    "X3 = subdata.iloc[:, 1:4]\n",
    "X4 = subdata.iloc[:, 1:]\n",
    "## 回归\n",
    "reg1 = linear_model.LinearRegression().fit(X1, y)\n",
    "reg2 = linear_model.LinearRegression().fit(X2, y)\n",
    "reg3 = linear_model.LinearRegression().fit(X3, y)\n",
    "reg4 = linear_model.LinearRegression().fit(X4, y)\n",
    "## 查看系数\n",
    "print(\"系数=\", reg1.coef_, '截距项=', reg1.intercept_)\n",
    "print(\"系数=\", reg2.coef_, '截距项=', reg2.intercept_)\n",
    "print(\"系数=\", reg3.coef_, '截距项=', reg3.intercept_)\n",
    "print(\"系数=\", reg4.coef_, '截距项=', reg4.intercept_)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接下来使用score函数计算$R^2$和$\\bar{R}^{2}$以及AIC、BIC："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'r2': 0.11174896601143425, 'r2_adjusted': 0.11172243046043973, 'aic': 211511.5561574294, 'bic': 211528.3933255157}\n",
      "{'r2': 0.20758038959695013, 'r2_adjusted': 0.20753304280339102, 'aic': 207691.8243099361, 'bic': 207717.08006206556}\n",
      "{'r2': 0.29857574006882537, 'r2_adjusted': 0.2985128734107293, 'aic': 203610.4841738876, 'bic': 203644.15851006022}\n",
      "{'r2': 0.2987764278913496, 'r2_adjusted': 0.2986926271597182, 'aic': 203602.90482625779, 'bic': 203644.99774647356}\n"
     ]
    }
   ],
   "source": [
    "def r2_ic(reg, x, y):\n",
    "    N = x.shape[0]\n",
    "    K = x.shape[1] + 1\n",
    "    result = dict()\n",
    "    result['r2'] = reg.score(x, y)\n",
    "    result['r2_adjusted'] = 1 - (N - 1) / (N - K) * (1 - result['r2'])\n",
    "    ## 计算似然函数\n",
    "    e_hat = y - reg.predict(x)\n",
    "    sigma2_hat = np.sum(e_hat**2) / N\n",
    "    ll = -0.5 * N * np.log(sigma2_hat)\n",
    "    result['aic'] = -2 * ll + 2 * K\n",
    "    result['bic'] = -2 * ll + np.log(N) * K\n",
    "    return result\n",
    "\n",
    "\n",
    "print(r2_ic(reg1, X1, y))\n",
    "print(r2_ic(reg2, X2, y))\n",
    "print(r2_ic(reg3, X3, y))\n",
    "print(r2_ic(reg4, X4, y))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "为了展示使用以上标准进行模型选择的过程，我们利用以上函数使用一个模拟的数据，对多项式的阶数进行选择。\n",
    "\n",
    "在以下程序中，我们首先产生了一个伪数据集：$$y=e^{x}+u$$其中$x\\sim U\\left(0,3\\right)$，$u\\sim N\\left(0,4\\right)$。接着，我们从x的一次方开始，逐渐向回归中添加$x^{2},x^{3},...,x^{10}$对模型进行拟合，首先生成数据："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y</th>\n",
       "      <th>x1</th>\n",
       "      <th>x2</th>\n",
       "      <th>x3</th>\n",
       "      <th>x4</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.188967</td>\n",
       "      <td>0.996738</td>\n",
       "      <td>0.993487</td>\n",
       "      <td>0.990246</td>\n",
       "      <td>0.987016</td>\n",
       "      <td>0.983797</td>\n",
       "      <td>0.980588</td>\n",
       "      <td>0.977390</td>\n",
       "      <td>0.974202</td>\n",
       "      <td>0.971024</td>\n",
       "      <td>0.967857</td>\n",
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       "      <th>1</th>\n",
       "      <td>7.257260</td>\n",
       "      <td>2.142828</td>\n",
       "      <td>4.591711</td>\n",
       "      <td>9.839245</td>\n",
       "      <td>21.083807</td>\n",
       "      <td>45.178965</td>\n",
       "      <td>96.810739</td>\n",
       "      <td>207.448736</td>\n",
       "      <td>444.526901</td>\n",
       "      <td>952.544567</td>\n",
       "      <td>2041.138904</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.187709</td>\n",
       "      <td>1.135042</td>\n",
       "      <td>1.288321</td>\n",
       "      <td>1.462300</td>\n",
       "      <td>1.659772</td>\n",
       "      <td>1.883912</td>\n",
       "      <td>2.138320</td>\n",
       "      <td>2.427084</td>\n",
       "      <td>2.754844</td>\n",
       "      <td>3.126864</td>\n",
       "      <td>3.549124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.669471</td>\n",
       "      <td>0.747247</td>\n",
       "      <td>0.558378</td>\n",
       "      <td>0.417247</td>\n",
       "      <td>0.311786</td>\n",
       "      <td>0.232982</td>\n",
       "      <td>0.174095</td>\n",
       "      <td>0.130092</td>\n",
       "      <td>0.097211</td>\n",
       "      <td>0.072640</td>\n",
       "      <td>0.054280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12.051389</td>\n",
       "      <td>2.507959</td>\n",
       "      <td>6.289858</td>\n",
       "      <td>15.774706</td>\n",
       "      <td>39.562317</td>\n",
       "      <td>99.220667</td>\n",
       "      <td>248.841364</td>\n",
       "      <td>624.083933</td>\n",
       "      <td>1565.176904</td>\n",
       "      <td>3925.399471</td>\n",
       "      <td>9844.740852</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           y        x1        x2         x3         x4         x5          x6  \\\n",
       "0   5.188967  0.996738  0.993487   0.990246   0.987016   0.983797    0.980588   \n",
       "1   7.257260  2.142828  4.591711   9.839245  21.083807  45.178965   96.810739   \n",
       "2   3.187709  1.135042  1.288321   1.462300   1.659772   1.883912    2.138320   \n",
       "3   0.669471  0.747247  0.558378   0.417247   0.311786   0.232982    0.174095   \n",
       "4  12.051389  2.507959  6.289858  15.774706  39.562317  99.220667  248.841364   \n",
       "\n",
       "           x7           x8           x9          x10  \n",
       "0    0.977390     0.974202     0.971024     0.967857  \n",
       "1  207.448736   444.526901   952.544567  2041.138904  \n",
       "2    2.427084     2.754844     3.126864     3.549124  \n",
       "3    0.130092     0.097211     0.072640     0.054280  \n",
       "4  624.083933  1565.176904  3925.399471  9844.740852  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N = 100  #样本量\n",
    "np.random.seed(1900)\n",
    "x = pd.Series(np.random.random(N)) * 3\n",
    "X = pd.DataFrame()\n",
    "X['y'] = np.exp(x) + np.random.normal(0, 2, N)\n",
    "for i in range(10):\n",
    "    var_name = 'x' + str(i + 1)\n",
    "    X[var_name] = x**(i + 1)\n",
    "\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接下来逐步加入多项式进行回归，并报告统计量："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " i  | R^2   |  R^2_a |  AIC   |  BIC \n",
      " 1 | 0.6895 | 0.6863 | 200.26 | 205.47\n",
      " 2 | 0.8194 | 0.8157 | 148.07 | 155.89\n",
      " 3 | 0.8266 | 0.8212 | 146.00 | 156.42\n",
      " 4 | 0.8282 | 0.8210 | 147.08 | 160.11\n",
      " 5 | 0.8284 | 0.8193 | 148.94 | 164.57\n",
      " 6 | 0.8294 | 0.8184 | 150.35 | 168.58\n",
      " 7 | 0.8295 | 0.8166 | 152.29 | 173.13\n",
      " 8 | 0.8302 | 0.8152 | 153.93 | 177.38\n",
      " 9 | 0.8308 | 0.8139 | 155.54 | 181.59\n",
      "10 | 0.8308 | 0.8118 | 157.54 | 186.19\n"
     ]
    }
   ],
   "source": [
    "print(\" i  | R^2   |  R^2_a |  AIC   |  BIC \")\n",
    "for i in range(10):\n",
    "    reg = linear_model.LinearRegression().fit(X.iloc[:, 1:i + 2], X['y'])\n",
    "    result = r2_ic(reg, X.iloc[:, 1:i + 2], X['y'])\n",
    "    print(\"%2d | %1.4f | %1.4f | %3.2f | %3.2f\" %\n",
    "          (i + 1, result['r2'], result['r2_adjusted'], result['aic'],\n",
    "           result['bic']))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从以上结果看出，以$\\bar{R}^{2}$为标准，似乎3阶多项式是比较好的；以AIC为标准，3阶多项式比较好；而以BIC为标准，似乎2阶多项式比较好。一般而言，BIC会挑选出比AIC更少的多项式阶数，或者更小的模型，不过在这里区别不大。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 交叉验证\n",
    "\n",
    "然而以上标准要么只适用于线性回归（调整的$R^2$），要么只适用于极大似然估计，通用性比较差，特别是很多机器学习方法并不依赖于统计方法，这些模型评价的指标通常不能直接用于机器学习中。\n",
    "\n",
    "注意到，不管是欠拟合还是过拟合，都会导致**样本外**预测的误差变大，因而我们可以只使用一部分样本进行估计，而在另外一部分样本中检验模型的预测能力。我们可以将样本分为两部分：**训练集**（**training set**）用于估计模型、**验证集**（**validation set**）用于评价模型的样本外预测能力。\n",
    "\n",
    "比如在以上多项式的例子中，我们可以首先随机将样本分为两部分，一部分训练模型一部分测试："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " i | R^2    |  MSE\n",
      " 1 | 0.6965 | 7.9009\n",
      " 2 | 0.8054 | 5.0663\n",
      " 3 | 0.7973 | 5.2770\n",
      " 4 | 0.7978 | 5.2640\n",
      " 5 | 0.7983 | 5.2493\n",
      " 6 | 0.7950 | 5.3352\n",
      " 7 | 0.7823 | 5.6670\n",
      " 8 | 0.7811 | 5.6976\n",
      " 9 | 0.7820 | 5.6759\n",
      "10 | 0.7721 | 5.9315\n"
     ]
    }
   ],
   "source": [
    "## 产生一个随机顺序，并排序，从而顺序是随机的\n",
    "X['random_order'] = np.random.random(N)\n",
    "X = X.sort_values(['random_order'])\n",
    "X = X.drop('random_order', axis=1)\n",
    "## 区分训练集和测试集\n",
    "X_train = X.iloc[0:80, :]\n",
    "X_test = X.iloc[80:, :]\n",
    "## 使用训练集回归\n",
    "print(\" i | R^2    |  MSE\")\n",
    "for i in range(10):\n",
    "    # 先使用训练集训练模型\n",
    "    reg = linear_model.LinearRegression().fit(X_train.iloc[:, 1:i + 2],\n",
    "                                              X_train['y'])\n",
    "    # 再使用测试集查看在测试集中预测的R2\n",
    "    r2 = reg.score(X_test.iloc[:, 1:i + 2], X_test['y'])\n",
    "    # 也可以计算均方误差\n",
    "    errors = X_test['y'] - reg.predict(X_test.iloc[:, 1:i + 2])\n",
    "    mse = np.mean(errors**2)\n",
    "    print(\"%2d | %1.4f | %1.4f\" % (i + 1, r2, mse))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "看起来似乎使用2阶多项式比较好。\n",
    "\n",
    "或者我们可以使用Scikit-Learn中的train_test_split函数自动帮我们区分训练集和测试集："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " i | R^2    |  MSE\n",
      " 1 | 0.7210 | 5.5723\n",
      " 2 | 0.8445 | 3.1064\n",
      " 3 | 0.8534 | 2.9268\n",
      " 4 | 0.8557 | 2.8814\n",
      " 5 | 0.8518 | 2.9595\n",
      " 6 | 0.8526 | 2.9447\n",
      " 7 | 0.8527 | 2.9411\n",
      " 8 | 0.8543 | 2.9089\n",
      " 9 | 0.8555 | 2.8868\n",
      "10 | 0.8505 | 2.9858\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "## 获取随机的训练集和测试集，选取20%的数据作为最终的测试集\n",
    "X_train, X_test, y_train, y_test = train_test_split(X.iloc[:, 1:],\n",
    "                                                    X['y'],\n",
    "                                                    test_size=0.2)\n",
    "## 使用训练集回归\n",
    "print(\" i | R^2    |  MSE\")\n",
    "for i in range(10):\n",
    "    # 先使用训练集训练模型\n",
    "    reg = linear_model.LinearRegression().fit(X_train.iloc[:, 0:i + 1],\n",
    "                                              y_train)\n",
    "    # 再使用测试集查看在测试集中预测的R2\n",
    "    r2 = reg.score(X_test.iloc[:, 0:i + 1], y_test)\n",
    "    # 计算均方误差\n",
    "    errors = y_test - reg.predict(X_test.iloc[:, 0:i + 1])\n",
    "    mse = np.mean(errors**2)\n",
    "    print(\"%2d | %1.4f | %1.4f\" % (i + 1, r2, mse))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "不过这样区分训练集和测试集具有随机性，为了克服这一问题，将以上方法进行推广，就得到了所谓的**交叉验证**（**cross validation**）。交叉验证常见的用法如：\n",
    "\n",
    "* **S折交叉验证**（**S-fold cross validation**）：将$N$个样本随机的分为大小相同的$S$组，然后利用$S-1$组的数据对数据进行拟合，并使用该模型对剩下的一组计算目标函数值（在这里即预测误差的度量，如误差平方）。将这一过程对$S$种组合重复进行，最终得到了$N$个目标函数值的加总（如均方误差）。对于不同的模型，选择使得验证集目标函数最优的那个，或者预测误差最小的那个模型。\n",
    "\n",
    "* **留一验证**（**leave-one-out cross validation**）：即$S=N$的$S$折交叉验证的特殊情形，每一次都用$N-1$个样本训练模型，对剩下的一个样本进行预测。\n",
    "\n",
    "可见，交叉验证的主要缺点是运算时间：对于$S$折交叉验证，必须重复估计模型$S$次，而对于留一验证，必须对模型估计$N$次。但是由于交叉验证度量了样本外预测的效果，因而可以有效避免欠拟合、过拟合。\n",
    "\n",
    "Scikit-learn里面包含了一些帮助我们完成交叉验证的方法，详情可参照：https://scikit-learn.org/stable/modules/cross_validation.html#cross-validation\n",
    "\n",
    "比如我们可以使用如下代码进行一个交叉验证的抽样："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---\n",
      " [20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43\n",
      " 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67\n",
      " 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91\n",
      " 92 93 94 95 96 97 98 99] \n",
      " [ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19]\n",
      "---\n",
      " [ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 40 41 42 43\n",
      " 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67\n",
      " 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91\n",
      " 92 93 94 95 96 97 98 99] \n",
      " [20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39]\n",
      "---\n",
      " [ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23\n",
      " 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 60 61 62 63 64 65 66 67\n",
      " 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91\n",
      " 92 93 94 95 96 97 98 99] \n",
      " [40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59]\n",
      "---\n",
      " [ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23\n",
      " 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47\n",
      " 48 49 50 51 52 53 54 55 56 57 58 59 80 81 82 83 84 85 86 87 88 89 90 91\n",
      " 92 93 94 95 96 97 98 99] \n",
      " [60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79]\n",
      "---\n",
      " [ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23\n",
      " 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47\n",
      " 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71\n",
      " 72 73 74 75 76 77 78 79] \n",
      " [80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "\n",
    "kf = KFold(n_splits=5)\n",
    "for train, test in kf.split(X):\n",
    "    print('---\\n', train, \"\\n\", test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接下来对每个训练集和测试集进行训练和验证（一个好的习惯是先随机排序），比如我们进行一个10折的交叉验证："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "预测误差=\n",
      " [-0.97941084  0.32874278  1.477193   -3.14933293  0.46618479  1.40394522\n",
      " -0.21679122  1.21180279  3.4972513  -1.56271975  1.84773268  0.51310592\n",
      " -2.08866055  0.13658632 -0.17915758  2.4379034  -0.34805687 -0.44027518\n",
      "  1.59031476 -0.80566504  0.64889446 -0.44540489  2.8423408  -0.8616925\n",
      "  0.12735213 -2.04989713 -2.87733855 -1.39231671  0.97359429  0.90986541\n",
      "  2.70436355 -0.57201358 -1.78529588  2.32833123  1.66283294 -1.83501106\n",
      " -1.11321433 -3.37964145  0.61982464 -0.03179838 -0.63886976 -0.28310296\n",
      "  1.35407805 -1.50679739 -1.79239822  3.7199329   1.39225659  2.30569115\n",
      " -0.28060709  0.22218465 -5.3584453   0.31956103  0.38424195  3.03171169\n",
      " -1.33503162  2.73426759 -0.4662434   0.560205   -2.63945096 -0.50945458\n",
      " -1.38566038  0.803114    1.3793609  -4.34613695  1.70817211 -0.13837416\n",
      "  4.21096444  0.88537031  0.04219259 -4.95117825  0.7421255  -0.26115026\n",
      " -0.41656705 -4.67789521 -0.59473336 -0.01690212  1.82349422  5.7589625\n",
      " -1.39339433 -4.34252865  0.85450942  1.03111986 -1.1397553  -0.65685293\n",
      " -3.24076097  2.18782303  5.2745927  -0.24446157 -4.81707257 -0.58814495\n",
      "  4.53148109  0.78275333  3.74828052  0.59474008  3.08925621 -0.13007291\n",
      " -0.76838144 -2.94042547 -2.4511454  -0.18668522]\n",
      "均方误差MSE= 4.746266884535841\n"
     ]
    }
   ],
   "source": [
    "## 产生一个随机顺序，并排序，从而顺序是随机的\n",
    "X['random_order'] = np.random.random(N)\n",
    "X = X.sort_values(['random_order'])\n",
    "X = X.drop('random_order', axis=1)\n",
    "## 区分训练集和测试集\n",
    "kf = KFold(n_splits=10)\n",
    "## 使用训练集回归\n",
    "CV_error = np.array([])\n",
    "for train, test in kf.split(X):\n",
    "    X_train = X.iloc[train, :]\n",
    "    X_test = X.iloc[test, :]\n",
    "    # 先使用训练集训练模型\n",
    "    reg = linear_model.LinearRegression().fit(X_train.iloc[:, 1:],\n",
    "                                              X_train['y'])\n",
    "    # 接下来在验证集上进行预测，得到预测误差\n",
    "    pre_error = X_test['y'] - reg.predict(X_test.iloc[:, 1:])\n",
    "    CV_error = np.concatenate([CV_error, pre_error])\n",
    "print(\"预测误差=\\n\", CV_error)\n",
    "print(\"均方误差MSE=\", np.mean(CV_error**2))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "为了评价所有阶数模型的交叉验证结果，可以嵌套一个循环："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " i | MSE\n",
      " 1 | 7.3474\n",
      " 2 | 4.3243\n",
      " 3 | 4.2543\n",
      " 4 | 4.2508\n",
      " 5 | 4.3852\n",
      " 6 | 4.4638\n",
      " 7 | 4.5507\n",
      " 8 | 4.6245\n",
      " 9 | 4.9105\n",
      "10 | 5.0725\n"
     ]
    }
   ],
   "source": [
    "## 产生一个随机顺序，并排序，从而顺序是随机的\n",
    "X['random_order'] = np.random.random(N)\n",
    "X = X.sort_values(['random_order'])\n",
    "X = X.drop('random_order', axis=1)\n",
    "## 区分训练集和测试集\n",
    "kf = KFold(n_splits=10)  ##设定为N则变成了留一验证\n",
    "print(\" i | MSE\")\n",
    "for i in range(10):\n",
    "    CV_error = np.array([])\n",
    "    for train, test in kf.split(X):\n",
    "        X_train = X.iloc[train, :]\n",
    "        X_test = X.iloc[test, :]\n",
    "        # 先使用训练集训练模型\n",
    "        reg = linear_model.LinearRegression().fit(X_train.iloc[:, 1:i + 2],\n",
    "                                                  X_train['y'])\n",
    "        # 接下来在验证集上进行预测，得到预测误差\n",
    "        pre_error = X_test['y'] - reg.predict(X_test.iloc[:, 1:i + 2])\n",
    "        CV_error = np.concatenate([CV_error, pre_error])\n",
    "    print(\"%2d | %1.4f\" % (i + 1, np.mean(CV_error**2)))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以见从交叉验证的角度来看，3阶多项式是预测效果比较理想的。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 模型选择的常用方法：正则化\n",
    "\n",
    "虽然交叉验证提供了一个评价模型的很好的标准，但是我们潜在的有非常多模型可以选择，而交叉验证本身就是非常耗时的一个计算，因而大多数情况我们并不会直接使用交叉验证去评价每一个潜在的模型。而对于大量的基于参数的模型（比如各种回归模型、神经网络模型等等），有一个比较通用的限制模型不能太多「大」的方法，即**正则化**（**regularization**）。\n",
    "\n",
    "正则化可以看做是**收缩估计量**（**shrinkage estimator**）的一种推广，其基本原理是统计学中经典的**偏差-方差权衡**（**bias-variance tradeof**）。\n",
    "\n",
    "对于预测问题，一个通常的性能度量是所谓的「**均方误差**」（**mean squared error, MSE**）：$$MSE=E\\left[y-f\\left(x\\right)\\right]^2$$而均方误差可以被分解为偏差的平方加上方差：$$MSE=Bias^2+Variance$$。问题是，bias和variance通常不太可能同时降低，如果要做到无偏，那么variance就会变大；如果想要降低variance，bias也会变大，就产生了矛盾。\n",
    "\n",
    "原始的统计学中，对于无偏性（或者渐进无偏性）是非常执着的，通常要求预测无偏，然而很快统计学家们发现无偏并不代表$MSE$最小，如果放弃无偏能够导致方差大幅度减小，反而会改进MSE。\n",
    "\n",
    "这一点可以从以下例子中看到。比如如果我们需要评估10个学生的真实计算机水平（图中黑点），但是我们只进行了一次考试，考试成绩为图中的红点。传统的OLS或者MLE告诉我们，红点是黑点的无偏估计。然而Stein(1956)指出红点任何时候都不是最好的预测，并进一步在James and Stein(1961)中提出了收缩估计量，即James-Stein估计量，如下图中的白点。\n",
    "\n",
    "![](pic/shrinkage.png \"收缩估计量\")\n",
    "\n",
    "可以看到白色的点虽然不是无偏的，但是预测效果却好很多，即通过放弃无偏性，降低方差，从而使得总的MSE降低。\n",
    "\n",
    "更令人震惊的是，James-Stein估计量并不要求向任何一个具体的点收缩，只要按照他们的方法进行收缩，那么就可以使得MSE降低。也就是说，**重要的是收缩本身，而不是往哪里收缩**。\n",
    "\n",
    "Stein的发现导致了统计学的一场重大变革，从此经验贝叶斯以及收缩估计量蓬勃发展起来。\n",
    "\n",
    "比如，扩展到线性回归上，常见的模型有**岭回归**（**ridge regression**）和**LASSO回归**（**Least absolute shrinkage and selection operator**）。\n",
    "\n",
    "其中岭回归的最优化问题为：$$\\hat{\\beta}^{r}\\left(\\lambda\\right)=\\arg\\min_{\\beta}\\sum_{i=1}^{N}\\left(y_{i}-x_{i}'\\beta\\right)^{2}+\\lambda\\left\\Vert \\beta\\right\\Vert _{2}^{2}=\\arg\\min_{\\beta}\\left(Y-X\\beta\\right)'\\left(Y-X\\beta\\right)+\\lambda\\beta'\\beta$$\n",
    "\n",
    "而Lasso回归的优化问题为：$$\\hat{\\beta}^{lasso}\\left(\\lambda\\right)=\\arg\\min_{\\beta}\\sum_{i=1}^{N}\\left(y_{i}-x_{i}'\\beta\\right)^{2}+\\lambda\\left\\Vert \\beta\\right\\Vert _{1}=\\arg\\min_{\\beta}\\sum_{i=1}^{N}\\left(y_{i}-x_{i}'\\beta\\right)^{2}+\\lambda \\sum_{k=1}^{K}\\left|\\beta_{k}\\right|$$\n",
    "\n",
    "两者的区别在于使用的「惩罚项」或者「正则化项」不同，岭回归使用了L2范数，而Lasso回归使用了L1范数，注意点不同导致了其估计量有非常不同的性质：\n",
    "\n",
    "![](pic/ridge_lasso.png \"岭回归和Lasso回归\")\n",
    "\n",
    "可以看到，虽然岭回归都会使得系数向0收缩，但是很难完全收缩到0；而Lasso回归则会使得部分系数直接变为0。因而**Lasso回归在进行收缩的同时，完成了变量选择**，或者说挑出了一个比较小的模型。\n",
    "\n",
    "正是由于这些特点，在高维问题上（$K\\approx N$或者$K\\geq N$）的情况下，岭回归和Lasso回归仍然可以使用。当然，由于Lasso还额外可以做变量选择，因而用的更多。\n",
    "\n",
    "注意其中涉及到一个参数$\\lambda$的选取，由于只是一个一维参数，从而可以很方便的使用交叉验证法进行选取。\n",
    "\n",
    "此外，需要提示的是，由于上述方法都是基于参数的大小的，因而对数据进行标准化（通常减均值除以标准差）就非常重要了！！否则，方差小的变量系数会更大，更容易被筛选出来。因而做岭回归、Lasso回归之前，**一定要对数据进行标准化**。\n",
    "\n",
    "在Lasso的基础上后续还发展了很多扩展的方法，比如adaptive Lasso、rLasso等等，我们在此不再赘述。\n",
    "\n",
    "在机器学习领域，其实多数参数模型包括Logistic回归、神经网络等等，都可以在目标函数后面加入L1范数进行特征（节点）的选取。\n",
    "\n",
    "最后，由于使用L1范数的正则化方法相当于在原始的最优化问题上加了一个约束，而这个约束的可行集是一个凸集，在很多算法里面，结合L1范数正则化的优化问题是一个凸优化（convex optimization）问题，在优化理论和计算机算法中有成熟的理论和算法解决这类优化问题，因而在算法层面也是完全可行的。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 一个预测实例\n",
    "\n",
    "线性回归是非常经典的对连续因变量预测的方法，当然在Scikit-Learn中自然也有非常成熟的应用。为了展示Python中线性回归的用法，我们不妨以计量经济学中的HCW方法（Hsiao, Ching and Wan, 2012）为例。\n",
    "\n",
    "比如，为了评价香港回归祖国对于香港经济的影响，我们可以利用其它国家和地区的经济增长率，使用1997年之前的数据进行回归，最后预测1997年之后香港的GDP增速，并与现实的香港GDP增速相比较。下面给出了具体过程，具体使用方法可以查询文档（https://scikit-learn.org/stable/modules/linear_model.html#lasso ）。\n",
    "\n",
    "首先，我们先准备数据："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>HongKong</th>\n",
       "      <th>Australia</th>\n",
       "      <th>Austria</th>\n",
       "      <th>Canada</th>\n",
       "      <th>Denmark</th>\n",
       "      <th>Finland</th>\n",
       "      <th>France</th>\n",
       "      <th>Germany</th>\n",
       "      <th>Italy</th>\n",
       "      <th>...</th>\n",
       "      <th>Switzerland</th>\n",
       "      <th>UnitedKingdom</th>\n",
       "      <th>UnitedStates</th>\n",
       "      <th>Singapore</th>\n",
       "      <th>Philippines</th>\n",
       "      <th>Indonesia</th>\n",
       "      <th>Malaysia</th>\n",
       "      <th>Thailand</th>\n",
       "      <th>Taiwan</th>\n",
       "      <th>China</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1993q1</td>\n",
       "      <td>0.062</td>\n",
       "      <td>0.040489</td>\n",
       "      <td>-0.013084</td>\n",
       "      <td>0.010064</td>\n",
       "      <td>-0.012292</td>\n",
       "      <td>-0.028357</td>\n",
       "      <td>-0.015177</td>\n",
       "      <td>-0.019680</td>\n",
       "      <td>-0.023383</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.032865</td>\n",
       "      <td>0.015124</td>\n",
       "      <td>0.022959</td>\n",
       "      <td>0.087145</td>\n",
       "      <td>-0.004381</td>\n",
       "      <td>0.064024</td>\n",
       "      <td>0.085938</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.064902</td>\n",
       "      <td>0.143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1993q2</td>\n",
       "      <td>0.059</td>\n",
       "      <td>0.037857</td>\n",
       "      <td>-0.007581</td>\n",
       "      <td>0.021264</td>\n",
       "      <td>-0.003093</td>\n",
       "      <td>-0.023397</td>\n",
       "      <td>-0.014549</td>\n",
       "      <td>-0.015441</td>\n",
       "      <td>-0.018116</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.019818</td>\n",
       "      <td>0.014795</td>\n",
       "      <td>0.018936</td>\n",
       "      <td>0.118075</td>\n",
       "      <td>0.016636</td>\n",
       "      <td>0.066068</td>\n",
       "      <td>0.131189</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.065123</td>\n",
       "      <td>0.141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1993q3</td>\n",
       "      <td>0.058</td>\n",
       "      <td>0.022509</td>\n",
       "      <td>0.000543</td>\n",
       "      <td>0.018919</td>\n",
       "      <td>-0.007764</td>\n",
       "      <td>-0.006018</td>\n",
       "      <td>-0.016704</td>\n",
       "      <td>-0.012701</td>\n",
       "      <td>-0.016875</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.004587</td>\n",
       "      <td>0.029149</td>\n",
       "      <td>0.017990</td>\n",
       "      <td>0.111130</td>\n",
       "      <td>0.031504</td>\n",
       "      <td>0.057959</td>\n",
       "      <td>0.109666</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.067379</td>\n",
       "      <td>0.135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1993q4</td>\n",
       "      <td>0.062</td>\n",
       "      <td>0.028747</td>\n",
       "      <td>0.001181</td>\n",
       "      <td>0.025317</td>\n",
       "      <td>-0.004049</td>\n",
       "      <td>-0.004774</td>\n",
       "      <td>-0.007476</td>\n",
       "      <td>-0.011667</td>\n",
       "      <td>-0.004963</td>\n",
       "      <td>...</td>\n",
       "      <td>0.013651</td>\n",
       "      <td>0.036581</td>\n",
       "      <td>0.020683</td>\n",
       "      <td>0.125324</td>\n",
       "      <td>0.034007</td>\n",
       "      <td>0.062365</td>\n",
       "      <td>0.075801</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.069164</td>\n",
       "      <td>0.135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1994q1</td>\n",
       "      <td>0.079</td>\n",
       "      <td>0.033990</td>\n",
       "      <td>0.025511</td>\n",
       "      <td>0.043567</td>\n",
       "      <td>0.031094</td>\n",
       "      <td>0.012886</td>\n",
       "      <td>0.003748</td>\n",
       "      <td>0.022950</td>\n",
       "      <td>-0.002249</td>\n",
       "      <td>...</td>\n",
       "      <td>0.026644</td>\n",
       "      <td>0.030078</td>\n",
       "      <td>0.029918</td>\n",
       "      <td>0.130709</td>\n",
       "      <td>0.049344</td>\n",
       "      <td>0.049743</td>\n",
       "      <td>0.049147</td>\n",
       "      <td>0.112509</td>\n",
       "      <td>0.069451</td>\n",
       "      <td>0.125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     time  HongKong  Australia   Austria    Canada   Denmark   Finland  \\\n",
       "0  1993q1     0.062   0.040489 -0.013084  0.010064 -0.012292 -0.028357   \n",
       "1  1993q2     0.059   0.037857 -0.007581  0.021264 -0.003093 -0.023397   \n",
       "2  1993q3     0.058   0.022509  0.000543  0.018919 -0.007764 -0.006018   \n",
       "3  1993q4     0.062   0.028747  0.001181  0.025317 -0.004049 -0.004774   \n",
       "4  1994q1     0.079   0.033990  0.025511  0.043567  0.031094  0.012886   \n",
       "\n",
       "     France   Germany     Italy  ...  Switzerland  UnitedKingdom  \\\n",
       "0 -0.015177 -0.019680 -0.023383  ...    -0.032865       0.015124   \n",
       "1 -0.014549 -0.015441 -0.018116  ...    -0.019818       0.014795   \n",
       "2 -0.016704 -0.012701 -0.016875  ...    -0.004587       0.029149   \n",
       "3 -0.007476 -0.011667 -0.004963  ...     0.013651       0.036581   \n",
       "4  0.003748  0.022950 -0.002249  ...     0.026644       0.030078   \n",
       "\n",
       "   UnitedStates  Singapore  Philippines  Indonesia  Malaysia  Thailand  \\\n",
       "0      0.022959   0.087145    -0.004381   0.064024  0.085938  0.080000   \n",
       "1      0.018936   0.118075     0.016636   0.066068  0.131189  0.080000   \n",
       "2      0.017990   0.111130     0.031504   0.057959  0.109666  0.080000   \n",
       "3      0.020683   0.125324     0.034007   0.062365  0.075801  0.080000   \n",
       "4      0.029918   0.130709     0.049344   0.049743  0.049147  0.112509   \n",
       "\n",
       "     Taiwan  China  \n",
       "0  0.064902  0.143  \n",
       "1  0.065123  0.141  \n",
       "2  0.067379  0.135  \n",
       "3  0.069164  0.135  \n",
       "4  0.069451  0.125  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "raw_data = pd.read_csv('csv/hcw.csv')\n",
    "raw_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_54132/426571931.py:1: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  data = raw_data.set_index(pd.to_datetime(raw_data['time']))\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>HongKong</th>\n",
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       "      <th>Denmark</th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1993Q1</th>\n",
       "      <td>1993q1</td>\n",
       "      <td>0.062</td>\n",
       "      <td>0.040489</td>\n",
       "      <td>-0.013084</td>\n",
       "      <td>0.010064</td>\n",
       "      <td>-0.012292</td>\n",
       "      <td>-0.028357</td>\n",
       "      <td>-0.015177</td>\n",
       "      <td>-0.019680</td>\n",
       "      <td>-0.023383</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.032865</td>\n",
       "      <td>0.015124</td>\n",
       "      <td>0.022959</td>\n",
       "      <td>0.087145</td>\n",
       "      <td>-0.004381</td>\n",
       "      <td>0.064024</td>\n",
       "      <td>0.085938</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.064902</td>\n",
       "      <td>0.1430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993Q2</th>\n",
       "      <td>1993q2</td>\n",
       "      <td>0.059</td>\n",
       "      <td>0.037857</td>\n",
       "      <td>-0.007581</td>\n",
       "      <td>0.021264</td>\n",
       "      <td>-0.003093</td>\n",
       "      <td>-0.023397</td>\n",
       "      <td>-0.014549</td>\n",
       "      <td>-0.015441</td>\n",
       "      <td>-0.018116</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.019818</td>\n",
       "      <td>0.014795</td>\n",
       "      <td>0.018936</td>\n",
       "      <td>0.118075</td>\n",
       "      <td>0.016636</td>\n",
       "      <td>0.066068</td>\n",
       "      <td>0.131189</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.065123</td>\n",
       "      <td>0.1410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993Q3</th>\n",
       "      <td>1993q3</td>\n",
       "      <td>0.058</td>\n",
       "      <td>0.022509</td>\n",
       "      <td>0.000543</td>\n",
       "      <td>0.018919</td>\n",
       "      <td>-0.007764</td>\n",
       "      <td>-0.006018</td>\n",
       "      <td>-0.016704</td>\n",
       "      <td>-0.012701</td>\n",
       "      <td>-0.016875</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.004587</td>\n",
       "      <td>0.029149</td>\n",
       "      <td>0.017990</td>\n",
       "      <td>0.111130</td>\n",
       "      <td>0.031504</td>\n",
       "      <td>0.057959</td>\n",
       "      <td>0.109666</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.067379</td>\n",
       "      <td>0.1350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993Q4</th>\n",
       "      <td>1993q4</td>\n",
       "      <td>0.062</td>\n",
       "      <td>0.028747</td>\n",
       "      <td>0.001181</td>\n",
       "      <td>0.025317</td>\n",
       "      <td>-0.004049</td>\n",
       "      <td>-0.004774</td>\n",
       "      <td>-0.007476</td>\n",
       "      <td>-0.011667</td>\n",
       "      <td>-0.004963</td>\n",
       "      <td>...</td>\n",
       "      <td>0.013651</td>\n",
       "      <td>0.036581</td>\n",
       "      <td>0.020683</td>\n",
       "      <td>0.125324</td>\n",
       "      <td>0.034007</td>\n",
       "      <td>0.062365</td>\n",
       "      <td>0.075801</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.069164</td>\n",
       "      <td>0.1350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994Q1</th>\n",
       "      <td>1994q1</td>\n",
       "      <td>0.079</td>\n",
       "      <td>0.033990</td>\n",
       "      <td>0.025511</td>\n",
       "      <td>0.043567</td>\n",
       "      <td>0.031094</td>\n",
       "      <td>0.012886</td>\n",
       "      <td>0.003748</td>\n",
       "      <td>0.022950</td>\n",
       "      <td>-0.002249</td>\n",
       "      <td>...</td>\n",
       "      <td>0.026644</td>\n",
       "      <td>0.030078</td>\n",
       "      <td>0.029918</td>\n",
       "      <td>0.130709</td>\n",
       "      <td>0.049344</td>\n",
       "      <td>0.049743</td>\n",
       "      <td>0.049147</td>\n",
       "      <td>0.112509</td>\n",
       "      <td>0.069451</td>\n",
       "      <td>0.1250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007Q1</th>\n",
       "      <td>2007q1</td>\n",
       "      <td>0.055</td>\n",
       "      <td>0.058013</td>\n",
       "      <td>0.036198</td>\n",
       "      <td>0.030712</td>\n",
       "      <td>0.033134</td>\n",
       "      <td>0.047340</td>\n",
       "      <td>0.032755</td>\n",
       "      <td>0.035225</td>\n",
       "      <td>0.025621</td>\n",
       "      <td>...</td>\n",
       "      <td>0.046517</td>\n",
       "      <td>0.027946</td>\n",
       "      <td>0.017708</td>\n",
       "      <td>0.063092</td>\n",
       "      <td>0.065573</td>\n",
       "      <td>0.095297</td>\n",
       "      <td>0.039884</td>\n",
       "      <td>0.049846</td>\n",
       "      <td>0.041019</td>\n",
       "      <td>0.1110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007Q2</th>\n",
       "      <td>2007q2</td>\n",
       "      <td>0.062</td>\n",
       "      <td>0.059519</td>\n",
       "      <td>0.032570</td>\n",
       "      <td>0.039827</td>\n",
       "      <td>-0.007169</td>\n",
       "      <td>0.046808</td>\n",
       "      <td>0.030355</td>\n",
       "      <td>0.023897</td>\n",
       "      <td>0.017251</td>\n",
       "      <td>...</td>\n",
       "      <td>0.046339</td>\n",
       "      <td>0.039619</td>\n",
       "      <td>0.018756</td>\n",
       "      <td>0.101377</td>\n",
       "      <td>0.081891</td>\n",
       "      <td>0.110900</td>\n",
       "      <td>0.080276</td>\n",
       "      <td>0.051197</td>\n",
       "      <td>0.051073</td>\n",
       "      <td>0.1167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007Q3</th>\n",
       "      <td>2007q3</td>\n",
       "      <td>0.068</td>\n",
       "      <td>0.056649</td>\n",
       "      <td>0.031558</td>\n",
       "      <td>0.034742</td>\n",
       "      <td>0.013517</td>\n",
       "      <td>0.045647</td>\n",
       "      <td>0.036748</td>\n",
       "      <td>0.020773</td>\n",
       "      <td>0.023338</td>\n",
       "      <td>...</td>\n",
       "      <td>0.040958</td>\n",
       "      <td>0.038682</td>\n",
       "      <td>0.028225</td>\n",
       "      <td>0.105562</td>\n",
       "      <td>0.064359</td>\n",
       "      <td>0.110100</td>\n",
       "      <td>0.093361</td>\n",
       "      <td>0.050126</td>\n",
       "      <td>0.066369</td>\n",
       "      <td>0.1002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007Q4</th>\n",
       "      <td>2007q4</td>\n",
       "      <td>0.069</td>\n",
       "      <td>0.045825</td>\n",
       "      <td>0.019095</td>\n",
       "      <td>0.038128</td>\n",
       "      <td>0.023794</td>\n",
       "      <td>0.044177</td>\n",
       "      <td>0.021745</td>\n",
       "      <td>0.005865</td>\n",
       "      <td>0.005081</td>\n",
       "      <td>...</td>\n",
       "      <td>0.037792</td>\n",
       "      <td>0.033362</td>\n",
       "      <td>0.009288</td>\n",
       "      <td>0.069739</td>\n",
       "      <td>0.065470</td>\n",
       "      <td>0.110700</td>\n",
       "      <td>0.151739</td>\n",
       "      <td>0.079587</td>\n",
       "      <td>0.062929</td>\n",
       "      <td>0.1017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008Q1</th>\n",
       "      <td>2008q1</td>\n",
       "      <td>0.073</td>\n",
       "      <td>0.027523</td>\n",
       "      <td>0.017431</td>\n",
       "      <td>0.029217</td>\n",
       "      <td>-0.005200</td>\n",
       "      <td>0.023732</td>\n",
       "      <td>0.018386</td>\n",
       "      <td>0.009020</td>\n",
       "      <td>-0.005581</td>\n",
       "      <td>...</td>\n",
       "      <td>0.028398</td>\n",
       "      <td>0.028492</td>\n",
       "      <td>0.006137</td>\n",
       "      <td>0.079339</td>\n",
       "      <td>0.047945</td>\n",
       "      <td>0.133400</td>\n",
       "      <td>0.167034</td>\n",
       "      <td>0.049144</td>\n",
       "      <td>0.058841</td>\n",
       "      <td>0.1238</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          time  HongKong  Australia   Austria    Canada   Denmark   Finland  \\\n",
       "time                                                                          \n",
       "1993Q1  1993q1     0.062   0.040489 -0.013084  0.010064 -0.012292 -0.028357   \n",
       "1993Q2  1993q2     0.059   0.037857 -0.007581  0.021264 -0.003093 -0.023397   \n",
       "1993Q3  1993q3     0.058   0.022509  0.000543  0.018919 -0.007764 -0.006018   \n",
       "1993Q4  1993q4     0.062   0.028747  0.001181  0.025317 -0.004049 -0.004774   \n",
       "1994Q1  1994q1     0.079   0.033990  0.025511  0.043567  0.031094  0.012886   \n",
       "...        ...       ...        ...       ...       ...       ...       ...   \n",
       "2007Q1  2007q1     0.055   0.058013  0.036198  0.030712  0.033134  0.047340   \n",
       "2007Q2  2007q2     0.062   0.059519  0.032570  0.039827 -0.007169  0.046808   \n",
       "2007Q3  2007q3     0.068   0.056649  0.031558  0.034742  0.013517  0.045647   \n",
       "2007Q4  2007q4     0.069   0.045825  0.019095  0.038128  0.023794  0.044177   \n",
       "2008Q1  2008q1     0.073   0.027523  0.017431  0.029217 -0.005200  0.023732   \n",
       "\n",
       "          France   Germany     Italy  ...  Switzerland  UnitedKingdom  \\\n",
       "time                                  ...                               \n",
       "1993Q1 -0.015177 -0.019680 -0.023383  ...    -0.032865       0.015124   \n",
       "1993Q2 -0.014549 -0.015441 -0.018116  ...    -0.019818       0.014795   \n",
       "1993Q3 -0.016704 -0.012701 -0.016875  ...    -0.004587       0.029149   \n",
       "1993Q4 -0.007476 -0.011667 -0.004963  ...     0.013651       0.036581   \n",
       "1994Q1  0.003748  0.022950 -0.002249  ...     0.026644       0.030078   \n",
       "...          ...       ...       ...  ...          ...            ...   \n",
       "2007Q1  0.032755  0.035225  0.025621  ...     0.046517       0.027946   \n",
       "2007Q2  0.030355  0.023897  0.017251  ...     0.046339       0.039619   \n",
       "2007Q3  0.036748  0.020773  0.023338  ...     0.040958       0.038682   \n",
       "2007Q4  0.021745  0.005865  0.005081  ...     0.037792       0.033362   \n",
       "2008Q1  0.018386  0.009020 -0.005581  ...     0.028398       0.028492   \n",
       "\n",
       "        UnitedStates  Singapore  Philippines  Indonesia  Malaysia  Thailand  \\\n",
       "time                                                                          \n",
       "1993Q1      0.022959   0.087145    -0.004381   0.064024  0.085938  0.080000   \n",
       "1993Q2      0.018936   0.118075     0.016636   0.066068  0.131189  0.080000   \n",
       "1993Q3      0.017990   0.111130     0.031504   0.057959  0.109666  0.080000   \n",
       "1993Q4      0.020683   0.125324     0.034007   0.062365  0.075801  0.080000   \n",
       "1994Q1      0.029918   0.130709     0.049344   0.049743  0.049147  0.112509   \n",
       "...              ...        ...          ...        ...       ...       ...   \n",
       "2007Q1      0.017708   0.063092     0.065573   0.095297  0.039884  0.049846   \n",
       "2007Q2      0.018756   0.101377     0.081891   0.110900  0.080276  0.051197   \n",
       "2007Q3      0.028225   0.105562     0.064359   0.110100  0.093361  0.050126   \n",
       "2007Q4      0.009288   0.069739     0.065470   0.110700  0.151739  0.079587   \n",
       "2008Q1      0.006137   0.079339     0.047945   0.133400  0.167034  0.049144   \n",
       "\n",
       "          Taiwan   China  \n",
       "time                      \n",
       "1993Q1  0.064902  0.1430  \n",
       "1993Q2  0.065123  0.1410  \n",
       "1993Q3  0.067379  0.1350  \n",
       "1993Q4  0.069164  0.1350  \n",
       "1994Q1  0.069451  0.1250  \n",
       "...          ...     ...  \n",
       "2007Q1  0.041019  0.1110  \n",
       "2007Q2  0.051073  0.1167  \n",
       "2007Q3  0.066369  0.1002  \n",
       "2007Q4  0.062929  0.1017  \n",
       "2008Q1  0.058841  0.1238  \n",
       "\n",
       "[61 rows x 26 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = raw_data.set_index(pd.to_datetime(raw_data['time']))\n",
    "data.index = data.index.to_period('Q')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>HongKong</th>\n",
       "      <th>Australia</th>\n",
       "      <th>Austria</th>\n",
       "      <th>Canada</th>\n",
       "      <th>Denmark</th>\n",
       "      <th>Finland</th>\n",
       "      <th>France</th>\n",
       "      <th>Germany</th>\n",
       "      <th>Italy</th>\n",
       "      <th>...</th>\n",
       "      <th>Switzerland</th>\n",
       "      <th>UnitedKingdom</th>\n",
       "      <th>UnitedStates</th>\n",
       "      <th>Singapore</th>\n",
       "      <th>Philippines</th>\n",
       "      <th>Indonesia</th>\n",
       "      <th>Malaysia</th>\n",
       "      <th>Thailand</th>\n",
       "      <th>Taiwan</th>\n",
       "      <th>China</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1993Q1</th>\n",
       "      <td>1993q1</td>\n",
       "      <td>0.062</td>\n",
       "      <td>0.040489</td>\n",
       "      <td>-0.013084</td>\n",
       "      <td>0.010064</td>\n",
       "      <td>-0.012292</td>\n",
       "      <td>-0.028357</td>\n",
       "      <td>-0.015177</td>\n",
       "      <td>-0.019680</td>\n",
       "      <td>-0.023383</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.032865</td>\n",
       "      <td>0.015124</td>\n",
       "      <td>0.022959</td>\n",
       "      <td>0.087145</td>\n",
       "      <td>-0.004381</td>\n",
       "      <td>0.064024</td>\n",
       "      <td>0.085938</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.064902</td>\n",
       "      <td>0.143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993Q2</th>\n",
       "      <td>1993q2</td>\n",
       "      <td>0.059</td>\n",
       "      <td>0.037857</td>\n",
       "      <td>-0.007581</td>\n",
       "      <td>0.021264</td>\n",
       "      <td>-0.003093</td>\n",
       "      <td>-0.023397</td>\n",
       "      <td>-0.014549</td>\n",
       "      <td>-0.015441</td>\n",
       "      <td>-0.018116</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.019818</td>\n",
       "      <td>0.014795</td>\n",
       "      <td>0.018936</td>\n",
       "      <td>0.118075</td>\n",
       "      <td>0.016636</td>\n",
       "      <td>0.066068</td>\n",
       "      <td>0.131189</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.065123</td>\n",
       "      <td>0.141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993Q3</th>\n",
       "      <td>1993q3</td>\n",
       "      <td>0.058</td>\n",
       "      <td>0.022509</td>\n",
       "      <td>0.000543</td>\n",
       "      <td>0.018919</td>\n",
       "      <td>-0.007764</td>\n",
       "      <td>-0.006018</td>\n",
       "      <td>-0.016704</td>\n",
       "      <td>-0.012701</td>\n",
       "      <td>-0.016875</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.004587</td>\n",
       "      <td>0.029149</td>\n",
       "      <td>0.017990</td>\n",
       "      <td>0.111130</td>\n",
       "      <td>0.031504</td>\n",
       "      <td>0.057959</td>\n",
       "      <td>0.109666</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.067379</td>\n",
       "      <td>0.135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993Q4</th>\n",
       "      <td>1993q4</td>\n",
       "      <td>0.062</td>\n",
       "      <td>0.028747</td>\n",
       "      <td>0.001181</td>\n",
       "      <td>0.025317</td>\n",
       "      <td>-0.004049</td>\n",
       "      <td>-0.004774</td>\n",
       "      <td>-0.007476</td>\n",
       "      <td>-0.011667</td>\n",
       "      <td>-0.004963</td>\n",
       "      <td>...</td>\n",
       "      <td>0.013651</td>\n",
       "      <td>0.036581</td>\n",
       "      <td>0.020683</td>\n",
       "      <td>0.125324</td>\n",
       "      <td>0.034007</td>\n",
       "      <td>0.062365</td>\n",
       "      <td>0.075801</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.069164</td>\n",
       "      <td>0.135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994Q1</th>\n",
       "      <td>1994q1</td>\n",
       "      <td>0.079</td>\n",
       "      <td>0.033990</td>\n",
       "      <td>0.025511</td>\n",
       "      <td>0.043567</td>\n",
       "      <td>0.031094</td>\n",
       "      <td>0.012886</td>\n",
       "      <td>0.003748</td>\n",
       "      <td>0.022950</td>\n",
       "      <td>-0.002249</td>\n",
       "      <td>...</td>\n",
       "      <td>0.026644</td>\n",
       "      <td>0.030078</td>\n",
       "      <td>0.029918</td>\n",
       "      <td>0.130709</td>\n",
       "      <td>0.049344</td>\n",
       "      <td>0.049743</td>\n",
       "      <td>0.049147</td>\n",
       "      <td>0.112509</td>\n",
       "      <td>0.069451</td>\n",
       "      <td>0.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994Q2</th>\n",
       "      <td>1994q2</td>\n",
       "      <td>0.068</td>\n",
       "      <td>0.037919</td>\n",
       "      <td>0.019941</td>\n",
       "      <td>0.050225</td>\n",
       "      <td>0.064280</td>\n",
       "      <td>0.035090</td>\n",
       "      <td>0.016165</td>\n",
       "      <td>0.021070</td>\n",
       "      <td>0.011635</td>\n",
       "      <td>...</td>\n",
       "      <td>0.010768</td>\n",
       "      <td>0.040359</td>\n",
       "      <td>0.037840</td>\n",
       "      <td>0.100987</td>\n",
       "      <td>0.059130</td>\n",
       "      <td>0.071988</td>\n",
       "      <td>0.061173</td>\n",
       "      <td>0.092613</td>\n",
       "      <td>0.070135</td>\n",
       "      <td>0.120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994Q3</th>\n",
       "      <td>1994q3</td>\n",
       "      <td>0.046</td>\n",
       "      <td>0.052289</td>\n",
       "      <td>0.017088</td>\n",
       "      <td>0.065122</td>\n",
       "      <td>0.045955</td>\n",
       "      <td>0.035247</td>\n",
       "      <td>0.023915</td>\n",
       "      <td>0.020662</td>\n",
       "      <td>0.026412</td>\n",
       "      <td>...</td>\n",
       "      <td>0.013645</td>\n",
       "      <td>0.038045</td>\n",
       "      <td>0.035534</td>\n",
       "      <td>0.115984</td>\n",
       "      <td>0.062870</td>\n",
       "      <td>0.069357</td>\n",
       "      <td>0.101110</td>\n",
       "      <td>0.050375</td>\n",
       "      <td>0.069298</td>\n",
       "      <td>0.120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994Q4</th>\n",
       "      <td>1994q4</td>\n",
       "      <td>0.052</td>\n",
       "      <td>0.031071</td>\n",
       "      <td>0.023035</td>\n",
       "      <td>0.067331</td>\n",
       "      <td>0.055166</td>\n",
       "      <td>0.057251</td>\n",
       "      <td>0.029711</td>\n",
       "      <td>0.028744</td>\n",
       "      <td>0.034283</td>\n",
       "      <td>...</td>\n",
       "      <td>0.013112</td>\n",
       "      <td>0.045036</td>\n",
       "      <td>0.035411</td>\n",
       "      <td>0.092047</td>\n",
       "      <td>0.062091</td>\n",
       "      <td>0.070347</td>\n",
       "      <td>0.134978</td>\n",
       "      <td>0.093952</td>\n",
       "      <td>0.076049</td>\n",
       "      <td>0.126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995Q1</th>\n",
       "      <td>1995q1</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.008696</td>\n",
       "      <td>0.025293</td>\n",
       "      <td>0.050921</td>\n",
       "      <td>0.048057</td>\n",
       "      <td>0.068382</td>\n",
       "      <td>0.027446</td>\n",
       "      <td>0.016826</td>\n",
       "      <td>0.025394</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.004910</td>\n",
       "      <td>0.031924</td>\n",
       "      <td>0.026520</td>\n",
       "      <td>0.066013</td>\n",
       "      <td>0.052228</td>\n",
       "      <td>0.103778</td>\n",
       "      <td>0.130363</td>\n",
       "      <td>0.106779</td>\n",
       "      <td>0.071104</td>\n",
       "      <td>0.115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995Q2</th>\n",
       "      <td>1995q2</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.006774</td>\n",
       "      <td>0.021850</td>\n",
       "      <td>0.031525</td>\n",
       "      <td>0.011954</td>\n",
       "      <td>0.079265</td>\n",
       "      <td>0.021708</td>\n",
       "      <td>0.028715</td>\n",
       "      <td>0.022612</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000607</td>\n",
       "      <td>0.035790</td>\n",
       "      <td>0.012290</td>\n",
       "      <td>0.079020</td>\n",
       "      <td>0.041678</td>\n",
       "      <td>0.083252</td>\n",
       "      <td>0.119140</td>\n",
       "      <td>0.112031</td>\n",
       "      <td>0.069214</td>\n",
       "      <td>0.106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995Q3</th>\n",
       "      <td>1995q3</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.003028</td>\n",
       "      <td>0.018319</td>\n",
       "      <td>0.018180</td>\n",
       "      <td>0.020810</td>\n",
       "      <td>0.085122</td>\n",
       "      <td>0.014152</td>\n",
       "      <td>0.026246</td>\n",
       "      <td>0.028764</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009705</td>\n",
       "      <td>0.031839</td>\n",
       "      <td>0.017522</td>\n",
       "      <td>0.087519</td>\n",
       "      <td>0.032849</td>\n",
       "      <td>0.070873</td>\n",
       "      <td>0.065085</td>\n",
       "      <td>0.083972</td>\n",
       "      <td>0.063417</td>\n",
       "      <td>0.101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995Q4</th>\n",
       "      <td>1995q4</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.010982</td>\n",
       "      <td>0.013457</td>\n",
       "      <td>0.015166</td>\n",
       "      <td>0.008304</td>\n",
       "      <td>0.085290</td>\n",
       "      <td>0.007562</td>\n",
       "      <td>0.012739</td>\n",
       "      <td>0.027092</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.012894</td>\n",
       "      <td>0.017693</td>\n",
       "      <td>0.013191</td>\n",
       "      <td>0.079930</td>\n",
       "      <td>0.036557</td>\n",
       "      <td>0.076905</td>\n",
       "      <td>0.074479</td>\n",
       "      <td>0.045679</td>\n",
       "      <td>0.048752</td>\n",
       "      <td>0.105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996Q1</th>\n",
       "      <td>1996q1</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.038182</td>\n",
       "      <td>0.015387</td>\n",
       "      <td>0.007821</td>\n",
       "      <td>0.010102</td>\n",
       "      <td>0.049422</td>\n",
       "      <td>0.008412</td>\n",
       "      <td>0.000539</td>\n",
       "      <td>0.034350</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001842</td>\n",
       "      <td>0.033937</td>\n",
       "      <td>0.016644</td>\n",
       "      <td>0.111706</td>\n",
       "      <td>0.032692</td>\n",
       "      <td>0.038864</td>\n",
       "      <td>0.103842</td>\n",
       "      <td>0.005897</td>\n",
       "      <td>0.054664</td>\n",
       "      <td>0.102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996Q2</th>\n",
       "      <td>1996q2</td>\n",
       "      <td>0.036</td>\n",
       "      <td>0.034520</td>\n",
       "      <td>0.017336</td>\n",
       "      <td>0.011510</td>\n",
       "      <td>0.030859</td>\n",
       "      <td>0.029334</td>\n",
       "      <td>0.003447</td>\n",
       "      <td>-0.001448</td>\n",
       "      <td>0.016941</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006181</td>\n",
       "      <td>0.036520</td>\n",
       "      <td>0.030020</td>\n",
       "      <td>0.087513</td>\n",
       "      <td>0.046900</td>\n",
       "      <td>0.066380</td>\n",
       "      <td>0.092732</td>\n",
       "      <td>0.049586</td>\n",
       "      <td>0.059415</td>\n",
       "      <td>0.097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996Q3</th>\n",
       "      <td>1996q3</td>\n",
       "      <td>0.047</td>\n",
       "      <td>0.036673</td>\n",
       "      <td>0.013595</td>\n",
       "      <td>0.021660</td>\n",
       "      <td>0.040113</td>\n",
       "      <td>0.017545</td>\n",
       "      <td>0.008331</td>\n",
       "      <td>-0.000206</td>\n",
       "      <td>0.014925</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001992</td>\n",
       "      <td>0.040162</td>\n",
       "      <td>0.027754</td>\n",
       "      <td>0.043669</td>\n",
       "      <td>0.074279</td>\n",
       "      <td>0.088341</td>\n",
       "      <td>0.096904</td>\n",
       "      <td>0.060580</td>\n",
       "      <td>0.059393</td>\n",
       "      <td>0.094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996Q4</th>\n",
       "      <td>1996q4</td>\n",
       "      <td>0.059</td>\n",
       "      <td>0.038987</td>\n",
       "      <td>0.004195</td>\n",
       "      <td>0.024704</td>\n",
       "      <td>0.025694</td>\n",
       "      <td>0.020857</td>\n",
       "      <td>0.006623</td>\n",
       "      <td>0.004316</td>\n",
       "      <td>0.004136</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001819</td>\n",
       "      <td>0.037069</td>\n",
       "      <td>0.030190</td>\n",
       "      <td>0.060471</td>\n",
       "      <td>0.028700</td>\n",
       "      <td>0.129308</td>\n",
       "      <td>0.095856</td>\n",
       "      <td>0.043259</td>\n",
       "      <td>0.070151</td>\n",
       "      <td>0.096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997Q1</th>\n",
       "      <td>1997q1</td>\n",
       "      <td>0.058</td>\n",
       "      <td>0.025748</td>\n",
       "      <td>-0.001535</td>\n",
       "      <td>0.035775</td>\n",
       "      <td>0.029462</td>\n",
       "      <td>0.054267</td>\n",
       "      <td>0.004803</td>\n",
       "      <td>0.006649</td>\n",
       "      <td>0.005500</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.004652</td>\n",
       "      <td>0.038317</td>\n",
       "      <td>0.033215</td>\n",
       "      <td>0.055836</td>\n",
       "      <td>0.052475</td>\n",
       "      <td>0.130339</td>\n",
       "      <td>0.068248</td>\n",
       "      <td>-0.006867</td>\n",
       "      <td>0.063613</td>\n",
       "      <td>0.094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997Q2</th>\n",
       "      <td>1997q2</td>\n",
       "      <td>0.072</td>\n",
       "      <td>0.051863</td>\n",
       "      <td>-0.002021</td>\n",
       "      <td>0.038688</td>\n",
       "      <td>0.039856</td>\n",
       "      <td>0.055036</td>\n",
       "      <td>0.018441</td>\n",
       "      <td>0.006054</td>\n",
       "      <td>0.029754</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005537</td>\n",
       "      <td>0.039479</td>\n",
       "      <td>0.035988</td>\n",
       "      <td>0.089873</td>\n",
       "      <td>0.051605</td>\n",
       "      <td>0.100466</td>\n",
       "      <td>0.065035</td>\n",
       "      <td>-0.024812</td>\n",
       "      <td>0.060897</td>\n",
       "      <td>0.095</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          time  HongKong  Australia   Austria    Canada   Denmark   Finland  \\\n",
       "time                                                                          \n",
       "1993Q1  1993q1     0.062   0.040489 -0.013084  0.010064 -0.012292 -0.028357   \n",
       "1993Q2  1993q2     0.059   0.037857 -0.007581  0.021264 -0.003093 -0.023397   \n",
       "1993Q3  1993q3     0.058   0.022509  0.000543  0.018919 -0.007764 -0.006018   \n",
       "1993Q4  1993q4     0.062   0.028747  0.001181  0.025317 -0.004049 -0.004774   \n",
       "1994Q1  1994q1     0.079   0.033990  0.025511  0.043567  0.031094  0.012886   \n",
       "1994Q2  1994q2     0.068   0.037919  0.019941  0.050225  0.064280  0.035090   \n",
       "1994Q3  1994q3     0.046   0.052289  0.017088  0.065122  0.045955  0.035247   \n",
       "1994Q4  1994q4     0.052   0.031071  0.023035  0.067331  0.055166  0.057251   \n",
       "1995Q1  1995q1     0.037   0.008696  0.025293  0.050921  0.048057  0.068382   \n",
       "1995Q2  1995q2     0.029   0.006774  0.021850  0.031525  0.011954  0.079265   \n",
       "1995Q3  1995q3     0.012   0.003028  0.018319  0.018180  0.020810  0.085122   \n",
       "1995Q4  1995q4     0.015   0.010982  0.013457  0.015166  0.008304  0.085290   \n",
       "1996Q1  1996q1     0.025   0.038182  0.015387  0.007821  0.010102  0.049422   \n",
       "1996Q2  1996q2     0.036   0.034520  0.017336  0.011510  0.030859  0.029334   \n",
       "1996Q3  1996q3     0.047   0.036673  0.013595  0.021660  0.040113  0.017545   \n",
       "1996Q4  1996q4     0.059   0.038987  0.004195  0.024704  0.025694  0.020857   \n",
       "1997Q1  1997q1     0.058   0.025748 -0.001535  0.035775  0.029462  0.054267   \n",
       "1997Q2  1997q2     0.072   0.051863 -0.002021  0.038688  0.039856  0.055036   \n",
       "\n",
       "          France   Germany     Italy  ...  Switzerland  UnitedKingdom  \\\n",
       "time                                  ...                               \n",
       "1993Q1 -0.015177 -0.019680 -0.023383  ...    -0.032865       0.015124   \n",
       "1993Q2 -0.014549 -0.015441 -0.018116  ...    -0.019818       0.014795   \n",
       "1993Q3 -0.016704 -0.012701 -0.016875  ...    -0.004587       0.029149   \n",
       "1993Q4 -0.007476 -0.011667 -0.004963  ...     0.013651       0.036581   \n",
       "1994Q1  0.003748  0.022950 -0.002249  ...     0.026644       0.030078   \n",
       "1994Q2  0.016165  0.021070  0.011635  ...     0.010768       0.040359   \n",
       "1994Q3  0.023915  0.020662  0.026412  ...     0.013645       0.038045   \n",
       "1994Q4  0.029711  0.028744  0.034283  ...     0.013112       0.045036   \n",
       "1995Q1  0.027446  0.016826  0.025394  ...    -0.004910       0.031924   \n",
       "1995Q2  0.021708  0.028715  0.022612  ...    -0.000607       0.035790   \n",
       "1995Q3  0.014152  0.026246  0.028764  ...    -0.009705       0.031839   \n",
       "1995Q4  0.007562  0.012739  0.027092  ...    -0.012894       0.017693   \n",
       "1996Q1  0.008412  0.000539  0.034350  ...    -0.001842       0.033937   \n",
       "1996Q2  0.003447 -0.001448  0.016941  ...     0.006181       0.036520   \n",
       "1996Q3  0.008331 -0.000206  0.014925  ...    -0.001992       0.040162   \n",
       "1996Q4  0.006623  0.004316  0.004136  ...    -0.001819       0.037069   \n",
       "1997Q1  0.004803  0.006649  0.005500  ...    -0.004652       0.038317   \n",
       "1997Q2  0.018441  0.006054  0.029754  ...     0.005537       0.039479   \n",
       "\n",
       "        UnitedStates  Singapore  Philippines  Indonesia  Malaysia  Thailand  \\\n",
       "time                                                                          \n",
       "1993Q1      0.022959   0.087145    -0.004381   0.064024  0.085938  0.080000   \n",
       "1993Q2      0.018936   0.118075     0.016636   0.066068  0.131189  0.080000   \n",
       "1993Q3      0.017990   0.111130     0.031504   0.057959  0.109666  0.080000   \n",
       "1993Q4      0.020683   0.125324     0.034007   0.062365  0.075801  0.080000   \n",
       "1994Q1      0.029918   0.130709     0.049344   0.049743  0.049147  0.112509   \n",
       "1994Q2      0.037840   0.100987     0.059130   0.071988  0.061173  0.092613   \n",
       "1994Q3      0.035534   0.115984     0.062870   0.069357  0.101110  0.050375   \n",
       "1994Q4      0.035411   0.092047     0.062091   0.070347  0.134978  0.093952   \n",
       "1995Q1      0.026520   0.066013     0.052228   0.103778  0.130363  0.106779   \n",
       "1995Q2      0.012290   0.079020     0.041678   0.083252  0.119140  0.112031   \n",
       "1995Q3      0.017522   0.087519     0.032849   0.070873  0.065085  0.083972   \n",
       "1995Q4      0.013191   0.079930     0.036557   0.076905  0.074479  0.045679   \n",
       "1996Q1      0.016644   0.111706     0.032692   0.038864  0.103842  0.005897   \n",
       "1996Q2      0.030020   0.087513     0.046900   0.066380  0.092732  0.049586   \n",
       "1996Q3      0.027754   0.043669     0.074279   0.088341  0.096904  0.060580   \n",
       "1996Q4      0.030190   0.060471     0.028700   0.129308  0.095856  0.043259   \n",
       "1997Q1      0.033215   0.055836     0.052475   0.130339  0.068248 -0.006867   \n",
       "1997Q2      0.035988   0.089873     0.051605   0.100466  0.065035 -0.024812   \n",
       "\n",
       "          Taiwan  China  \n",
       "time                     \n",
       "1993Q1  0.064902  0.143  \n",
       "1993Q2  0.065123  0.141  \n",
       "1993Q3  0.067379  0.135  \n",
       "1993Q4  0.069164  0.135  \n",
       "1994Q1  0.069451  0.125  \n",
       "1994Q2  0.070135  0.120  \n",
       "1994Q3  0.069298  0.120  \n",
       "1994Q4  0.076049  0.126  \n",
       "1995Q1  0.071104  0.115  \n",
       "1995Q2  0.069214  0.106  \n",
       "1995Q3  0.063417  0.101  \n",
       "1995Q4  0.048752  0.105  \n",
       "1996Q1  0.054664  0.102  \n",
       "1996Q2  0.059415  0.097  \n",
       "1996Q3  0.059393  0.094  \n",
       "1996Q4  0.070151  0.096  \n",
       "1997Q1  0.063613  0.094  \n",
       "1997Q2  0.060897  0.095  \n",
       "\n",
       "[18 rows x 26 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pre1997 = data[data.index <= '1997Q2']\n",
    "pre1997"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Australia</th>\n",
       "      <th>France</th>\n",
       "      <th>Taiwan</th>\n",
       "      <th>Singapore</th>\n",
       "      <th>Denmark</th>\n",
       "      <th>Japan</th>\n",
       "      <th>Indonesia</th>\n",
       "      <th>Philippines</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1993Q1</th>\n",
       "      <td>0.040489</td>\n",
       "      <td>-0.015177</td>\n",
       "      <td>0.064902</td>\n",
       "      <td>0.087145</td>\n",
       "      <td>-0.012292</td>\n",
       "      <td>0.012683</td>\n",
       "      <td>0.064024</td>\n",
       "      <td>-0.004381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993Q2</th>\n",
       "      <td>0.037857</td>\n",
       "      <td>-0.014549</td>\n",
       "      <td>0.065123</td>\n",
       "      <td>0.118075</td>\n",
       "      <td>-0.003093</td>\n",
       "      <td>-0.005571</td>\n",
       "      <td>0.066068</td>\n",
       "      <td>0.016636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993Q3</th>\n",
       "      <td>0.022509</td>\n",
       "      <td>-0.016704</td>\n",
       "      <td>0.067379</td>\n",
       "      <td>0.111130</td>\n",
       "      <td>-0.007764</td>\n",
       "      <td>-0.017558</td>\n",
       "      <td>0.057959</td>\n",
       "      <td>0.031504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993Q4</th>\n",
       "      <td>0.028747</td>\n",
       "      <td>-0.007476</td>\n",
       "      <td>0.069164</td>\n",
       "      <td>0.125324</td>\n",
       "      <td>-0.004049</td>\n",
       "      <td>-0.010101</td>\n",
       "      <td>0.062365</td>\n",
       "      <td>0.034007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994Q1</th>\n",
       "      <td>0.033990</td>\n",
       "      <td>0.003748</td>\n",
       "      <td>0.069451</td>\n",
       "      <td>0.130709</td>\n",
       "      <td>0.031094</td>\n",
       "      <td>-0.022503</td>\n",
       "      <td>0.049743</td>\n",
       "      <td>0.049344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994Q2</th>\n",
       "      <td>0.037919</td>\n",
       "      <td>0.016165</td>\n",
       "      <td>0.070135</td>\n",
       "      <td>0.100987</td>\n",
       "      <td>0.064280</td>\n",
       "      <td>-0.005157</td>\n",
       "      <td>0.071988</td>\n",
       "      <td>0.059130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994Q3</th>\n",
       "      <td>0.052289</td>\n",
       "      <td>0.023915</td>\n",
       "      <td>0.069298</td>\n",
       "      <td>0.115984</td>\n",
       "      <td>0.045955</td>\n",
       "      <td>0.014087</td>\n",
       "      <td>0.069357</td>\n",
       "      <td>0.062870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994Q4</th>\n",
       "      <td>0.031071</td>\n",
       "      <td>0.029711</td>\n",
       "      <td>0.076049</td>\n",
       "      <td>0.092047</td>\n",
       "      <td>0.055166</td>\n",
       "      <td>0.005427</td>\n",
       "      <td>0.070347</td>\n",
       "      <td>0.062091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995Q1</th>\n",
       "      <td>0.008696</td>\n",
       "      <td>0.027446</td>\n",
       "      <td>0.071104</td>\n",
       "      <td>0.066013</td>\n",
       "      <td>0.048057</td>\n",
       "      <td>0.003919</td>\n",
       "      <td>0.103778</td>\n",
       "      <td>0.052228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995Q2</th>\n",
       "      <td>0.006774</td>\n",
       "      <td>0.021708</td>\n",
       "      <td>0.069214</td>\n",
       "      <td>0.079020</td>\n",
       "      <td>0.011954</td>\n",
       "      <td>0.015349</td>\n",
       "      <td>0.083252</td>\n",
       "      <td>0.041678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995Q3</th>\n",
       "      <td>0.003028</td>\n",
       "      <td>0.014152</td>\n",
       "      <td>0.063417</td>\n",
       "      <td>0.087519</td>\n",
       "      <td>0.020810</td>\n",
       "      <td>0.015939</td>\n",
       "      <td>0.070873</td>\n",
       "      <td>0.032849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995Q4</th>\n",
       "      <td>0.010982</td>\n",
       "      <td>0.007562</td>\n",
       "      <td>0.048752</td>\n",
       "      <td>0.079930</td>\n",
       "      <td>0.008304</td>\n",
       "      <td>0.026233</td>\n",
       "      <td>0.076905</td>\n",
       "      <td>0.036557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996Q1</th>\n",
       "      <td>0.038182</td>\n",
       "      <td>0.008412</td>\n",
       "      <td>0.054664</td>\n",
       "      <td>0.111706</td>\n",
       "      <td>0.010102</td>\n",
       "      <td>0.026878</td>\n",
       "      <td>0.038864</td>\n",
       "      <td>0.032692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996Q2</th>\n",
       "      <td>0.034520</td>\n",
       "      <td>0.003447</td>\n",
       "      <td>0.059415</td>\n",
       "      <td>0.087513</td>\n",
       "      <td>0.030859</td>\n",
       "      <td>0.022471</td>\n",
       "      <td>0.066380</td>\n",
       "      <td>0.046900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996Q3</th>\n",
       "      <td>0.036673</td>\n",
       "      <td>0.008331</td>\n",
       "      <td>0.059393</td>\n",
       "      <td>0.043669</td>\n",
       "      <td>0.040113</td>\n",
       "      <td>0.012733</td>\n",
       "      <td>0.088341</td>\n",
       "      <td>0.074279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996Q4</th>\n",
       "      <td>0.038987</td>\n",
       "      <td>0.006623</td>\n",
       "      <td>0.070151</td>\n",
       "      <td>0.060471</td>\n",
       "      <td>0.025694</td>\n",
       "      <td>0.021495</td>\n",
       "      <td>0.129308</td>\n",
       "      <td>0.028700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997Q1</th>\n",
       "      <td>0.025748</td>\n",
       "      <td>0.004803</td>\n",
       "      <td>0.063613</td>\n",
       "      <td>0.055836</td>\n",
       "      <td>0.029462</td>\n",
       "      <td>0.025330</td>\n",
       "      <td>0.130339</td>\n",
       "      <td>0.052475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997Q2</th>\n",
       "      <td>0.051863</td>\n",
       "      <td>0.018441</td>\n",
       "      <td>0.060897</td>\n",
       "      <td>0.089873</td>\n",
       "      <td>0.039856</td>\n",
       "      <td>0.006164</td>\n",
       "      <td>0.100466</td>\n",
       "      <td>0.051605</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Australia    France    Taiwan  Singapore   Denmark     Japan  \\\n",
       "time                                                                   \n",
       "1993Q1   0.040489 -0.015177  0.064902   0.087145 -0.012292  0.012683   \n",
       "1993Q2   0.037857 -0.014549  0.065123   0.118075 -0.003093 -0.005571   \n",
       "1993Q3   0.022509 -0.016704  0.067379   0.111130 -0.007764 -0.017558   \n",
       "1993Q4   0.028747 -0.007476  0.069164   0.125324 -0.004049 -0.010101   \n",
       "1994Q1   0.033990  0.003748  0.069451   0.130709  0.031094 -0.022503   \n",
       "1994Q2   0.037919  0.016165  0.070135   0.100987  0.064280 -0.005157   \n",
       "1994Q3   0.052289  0.023915  0.069298   0.115984  0.045955  0.014087   \n",
       "1994Q4   0.031071  0.029711  0.076049   0.092047  0.055166  0.005427   \n",
       "1995Q1   0.008696  0.027446  0.071104   0.066013  0.048057  0.003919   \n",
       "1995Q2   0.006774  0.021708  0.069214   0.079020  0.011954  0.015349   \n",
       "1995Q3   0.003028  0.014152  0.063417   0.087519  0.020810  0.015939   \n",
       "1995Q4   0.010982  0.007562  0.048752   0.079930  0.008304  0.026233   \n",
       "1996Q1   0.038182  0.008412  0.054664   0.111706  0.010102  0.026878   \n",
       "1996Q2   0.034520  0.003447  0.059415   0.087513  0.030859  0.022471   \n",
       "1996Q3   0.036673  0.008331  0.059393   0.043669  0.040113  0.012733   \n",
       "1996Q4   0.038987  0.006623  0.070151   0.060471  0.025694  0.021495   \n",
       "1997Q1   0.025748  0.004803  0.063613   0.055836  0.029462  0.025330   \n",
       "1997Q2   0.051863  0.018441  0.060897   0.089873  0.039856  0.006164   \n",
       "\n",
       "        Indonesia  Philippines  \n",
       "time                            \n",
       "1993Q1   0.064024    -0.004381  \n",
       "1993Q2   0.066068     0.016636  \n",
       "1993Q3   0.057959     0.031504  \n",
       "1993Q4   0.062365     0.034007  \n",
       "1994Q1   0.049743     0.049344  \n",
       "1994Q2   0.071988     0.059130  \n",
       "1994Q3   0.069357     0.062870  \n",
       "1994Q4   0.070347     0.062091  \n",
       "1995Q1   0.103778     0.052228  \n",
       "1995Q2   0.083252     0.041678  \n",
       "1995Q3   0.070873     0.032849  \n",
       "1995Q4   0.076905     0.036557  \n",
       "1996Q1   0.038864     0.032692  \n",
       "1996Q2   0.066380     0.046900  \n",
       "1996Q3   0.088341     0.074279  \n",
       "1996Q4   0.129308     0.028700  \n",
       "1997Q1   0.130339     0.052475  \n",
       "1997Q2   0.100466     0.051605  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = pre1997['HongKong']\n",
    "indep_var = [\n",
    "    'Australia', 'France', 'Taiwan', 'Singapore', 'Denmark', 'Japan',\n",
    "    'Indonesia', 'Philippines'\n",
    "]\n",
    "X = pre1997[indep_var]\n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面开始回归："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "系数= [ 0.73937958 -0.28910318  0.29587194 -0.02260917  0.11208418 -0.81544603\n",
      "  0.30000394 -0.09297764]\n",
      "截距项= -0.003890434533065368\n"
     ]
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "\n",
    "## 创建模型对象\n",
    "reg = linear_model.LinearRegression()\n",
    "## 进行回归\n",
    "reg.fit(X, y)  ## 训练模型\n",
    "## 查看系数\n",
    "print(\"系数=\", reg.coef_)\n",
    "print('截距项=', reg.intercept_)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果需要做预测，可以使用："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>HongKong</th>\n",
       "      <th>Australia</th>\n",
       "      <th>Austria</th>\n",
       "      <th>Canada</th>\n",
       "      <th>Denmark</th>\n",
       "      <th>Finland</th>\n",
       "      <th>France</th>\n",
       "      <th>Germany</th>\n",
       "      <th>Italy</th>\n",
       "      <th>...</th>\n",
       "      <th>UnitedKingdom</th>\n",
       "      <th>UnitedStates</th>\n",
       "      <th>Singapore</th>\n",
       "      <th>Philippines</th>\n",
       "      <th>Indonesia</th>\n",
       "      <th>Malaysia</th>\n",
       "      <th>Thailand</th>\n",
       "      <th>Taiwan</th>\n",
       "      <th>China</th>\n",
       "      <th>predict_HongKong</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1993Q1</th>\n",
       "      <td>1993q1</td>\n",
       "      <td>0.062</td>\n",
       "      <td>0.040489</td>\n",
       "      <td>-0.013084</td>\n",
       "      <td>0.010064</td>\n",
       "      <td>-0.012292</td>\n",
       "      <td>-0.028357</td>\n",
       "      <td>-0.015177</td>\n",
       "      <td>-0.019680</td>\n",
       "      <td>-0.023383</td>\n",
       "      <td>...</td>\n",
       "      <td>0.015124</td>\n",
       "      <td>0.022959</td>\n",
       "      <td>0.087145</td>\n",
       "      <td>-0.004381</td>\n",
       "      <td>0.064024</td>\n",
       "      <td>0.085938</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.064902</td>\n",
       "      <td>0.1430</td>\n",
       "      <td>0.055561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993Q2</th>\n",
       "      <td>1993q2</td>\n",
       "      <td>0.059</td>\n",
       "      <td>0.037857</td>\n",
       "      <td>-0.007581</td>\n",
       "      <td>0.021264</td>\n",
       "      <td>-0.003093</td>\n",
       "      <td>-0.023397</td>\n",
       "      <td>-0.014549</td>\n",
       "      <td>-0.015441</td>\n",
       "      <td>-0.018116</td>\n",
       "      <td>...</td>\n",
       "      <td>0.014795</td>\n",
       "      <td>0.018936</td>\n",
       "      <td>0.118075</td>\n",
       "      <td>0.016636</td>\n",
       "      <td>0.066068</td>\n",
       "      <td>0.131189</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.065123</td>\n",
       "      <td>0.1410</td>\n",
       "      <td>0.067375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993Q3</th>\n",
       "      <td>1993q3</td>\n",
       "      <td>0.058</td>\n",
       "      <td>0.022509</td>\n",
       "      <td>0.000543</td>\n",
       "      <td>0.018919</td>\n",
       "      <td>-0.007764</td>\n",
       "      <td>-0.006018</td>\n",
       "      <td>-0.016704</td>\n",
       "      <td>-0.012701</td>\n",
       "      <td>-0.016875</td>\n",
       "      <td>...</td>\n",
       "      <td>0.029149</td>\n",
       "      <td>0.017990</td>\n",
       "      <td>0.111130</td>\n",
       "      <td>0.031504</td>\n",
       "      <td>0.057959</td>\n",
       "      <td>0.109666</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.067379</td>\n",
       "      <td>0.1350</td>\n",
       "      <td>0.062911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993Q4</th>\n",
       "      <td>1993q4</td>\n",
       "      <td>0.062</td>\n",
       "      <td>0.028747</td>\n",
       "      <td>0.001181</td>\n",
       "      <td>0.025317</td>\n",
       "      <td>-0.004049</td>\n",
       "      <td>-0.004774</td>\n",
       "      <td>-0.007476</td>\n",
       "      <td>-0.011667</td>\n",
       "      <td>-0.004963</td>\n",
       "      <td>...</td>\n",
       "      <td>0.036581</td>\n",
       "      <td>0.020683</td>\n",
       "      <td>0.125324</td>\n",
       "      <td>0.034007</td>\n",
       "      <td>0.062365</td>\n",
       "      <td>0.075801</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.069164</td>\n",
       "      <td>0.1350</td>\n",
       "      <td>0.060487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994Q1</th>\n",
       "      <td>1994q1</td>\n",
       "      <td>0.079</td>\n",
       "      <td>0.033990</td>\n",
       "      <td>0.025511</td>\n",
       "      <td>0.043567</td>\n",
       "      <td>0.031094</td>\n",
       "      <td>0.012886</td>\n",
       "      <td>0.003748</td>\n",
       "      <td>0.022950</td>\n",
       "      <td>-0.002249</td>\n",
       "      <td>...</td>\n",
       "      <td>0.030078</td>\n",
       "      <td>0.029918</td>\n",
       "      <td>0.130709</td>\n",
       "      <td>0.049344</td>\n",
       "      <td>0.049743</td>\n",
       "      <td>0.049147</td>\n",
       "      <td>0.112509</td>\n",
       "      <td>0.069451</td>\n",
       "      <td>0.1250</td>\n",
       "      <td>0.069921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007Q1</th>\n",
       "      <td>2007q1</td>\n",
       "      <td>0.055</td>\n",
       "      <td>0.058013</td>\n",
       "      <td>0.036198</td>\n",
       "      <td>0.030712</td>\n",
       "      <td>0.033134</td>\n",
       "      <td>0.047340</td>\n",
       "      <td>0.032755</td>\n",
       "      <td>0.035225</td>\n",
       "      <td>0.025621</td>\n",
       "      <td>...</td>\n",
       "      <td>0.027946</td>\n",
       "      <td>0.017708</td>\n",
       "      <td>0.063092</td>\n",
       "      <td>0.065573</td>\n",
       "      <td>0.095297</td>\n",
       "      <td>0.039884</td>\n",
       "      <td>0.049846</td>\n",
       "      <td>0.041019</td>\n",
       "      <td>0.1110</td>\n",
       "      <td>0.046912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007Q2</th>\n",
       "      <td>2007q2</td>\n",
       "      <td>0.062</td>\n",
       "      <td>0.059519</td>\n",
       "      <td>0.032570</td>\n",
       "      <td>0.039827</td>\n",
       "      <td>-0.007169</td>\n",
       "      <td>0.046808</td>\n",
       "      <td>0.030355</td>\n",
       "      <td>0.023897</td>\n",
       "      <td>0.017251</td>\n",
       "      <td>...</td>\n",
       "      <td>0.039619</td>\n",
       "      <td>0.018756</td>\n",
       "      <td>0.101377</td>\n",
       "      <td>0.081891</td>\n",
       "      <td>0.110900</td>\n",
       "      <td>0.080276</td>\n",
       "      <td>0.051197</td>\n",
       "      <td>0.051073</td>\n",
       "      <td>0.1167</td>\n",
       "      <td>0.057439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007Q3</th>\n",
       "      <td>2007q3</td>\n",
       "      <td>0.068</td>\n",
       "      <td>0.056649</td>\n",
       "      <td>0.031558</td>\n",
       "      <td>0.034742</td>\n",
       "      <td>0.013517</td>\n",
       "      <td>0.045647</td>\n",
       "      <td>0.036748</td>\n",
       "      <td>0.020773</td>\n",
       "      <td>0.023338</td>\n",
       "      <td>...</td>\n",
       "      <td>0.038682</td>\n",
       "      <td>0.028225</td>\n",
       "      <td>0.105562</td>\n",
       "      <td>0.064359</td>\n",
       "      <td>0.110100</td>\n",
       "      <td>0.093361</td>\n",
       "      <td>0.050126</td>\n",
       "      <td>0.066369</td>\n",
       "      <td>0.1002</td>\n",
       "      <td>0.062657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007Q4</th>\n",
       "      <td>2007q4</td>\n",
       "      <td>0.069</td>\n",
       "      <td>0.045825</td>\n",
       "      <td>0.019095</td>\n",
       "      <td>0.038128</td>\n",
       "      <td>0.023794</td>\n",
       "      <td>0.044177</td>\n",
       "      <td>0.021745</td>\n",
       "      <td>0.005865</td>\n",
       "      <td>0.005081</td>\n",
       "      <td>...</td>\n",
       "      <td>0.033362</td>\n",
       "      <td>0.009288</td>\n",
       "      <td>0.069739</td>\n",
       "      <td>0.065470</td>\n",
       "      <td>0.110700</td>\n",
       "      <td>0.151739</td>\n",
       "      <td>0.079587</td>\n",
       "      <td>0.062929</td>\n",
       "      <td>0.1017</td>\n",
       "      <td>0.074425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008Q1</th>\n",
       "      <td>2008q1</td>\n",
       "      <td>0.073</td>\n",
       "      <td>0.027523</td>\n",
       "      <td>0.017431</td>\n",
       "      <td>0.029217</td>\n",
       "      <td>-0.005200</td>\n",
       "      <td>0.023732</td>\n",
       "      <td>0.018386</td>\n",
       "      <td>0.009020</td>\n",
       "      <td>-0.005581</td>\n",
       "      <td>...</td>\n",
       "      <td>0.028492</td>\n",
       "      <td>0.006137</td>\n",
       "      <td>0.079339</td>\n",
       "      <td>0.047945</td>\n",
       "      <td>0.133400</td>\n",
       "      <td>0.167034</td>\n",
       "      <td>0.049144</td>\n",
       "      <td>0.058841</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.072868</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          time  HongKong  Australia   Austria    Canada   Denmark   Finland  \\\n",
       "time                                                                          \n",
       "1993Q1  1993q1     0.062   0.040489 -0.013084  0.010064 -0.012292 -0.028357   \n",
       "1993Q2  1993q2     0.059   0.037857 -0.007581  0.021264 -0.003093 -0.023397   \n",
       "1993Q3  1993q3     0.058   0.022509  0.000543  0.018919 -0.007764 -0.006018   \n",
       "1993Q4  1993q4     0.062   0.028747  0.001181  0.025317 -0.004049 -0.004774   \n",
       "1994Q1  1994q1     0.079   0.033990  0.025511  0.043567  0.031094  0.012886   \n",
       "...        ...       ...        ...       ...       ...       ...       ...   \n",
       "2007Q1  2007q1     0.055   0.058013  0.036198  0.030712  0.033134  0.047340   \n",
       "2007Q2  2007q2     0.062   0.059519  0.032570  0.039827 -0.007169  0.046808   \n",
       "2007Q3  2007q3     0.068   0.056649  0.031558  0.034742  0.013517  0.045647   \n",
       "2007Q4  2007q4     0.069   0.045825  0.019095  0.038128  0.023794  0.044177   \n",
       "2008Q1  2008q1     0.073   0.027523  0.017431  0.029217 -0.005200  0.023732   \n",
       "\n",
       "          France   Germany     Italy  ...  UnitedKingdom  UnitedStates  \\\n",
       "time                                  ...                                \n",
       "1993Q1 -0.015177 -0.019680 -0.023383  ...       0.015124      0.022959   \n",
       "1993Q2 -0.014549 -0.015441 -0.018116  ...       0.014795      0.018936   \n",
       "1993Q3 -0.016704 -0.012701 -0.016875  ...       0.029149      0.017990   \n",
       "1993Q4 -0.007476 -0.011667 -0.004963  ...       0.036581      0.020683   \n",
       "1994Q1  0.003748  0.022950 -0.002249  ...       0.030078      0.029918   \n",
       "...          ...       ...       ...  ...            ...           ...   \n",
       "2007Q1  0.032755  0.035225  0.025621  ...       0.027946      0.017708   \n",
       "2007Q2  0.030355  0.023897  0.017251  ...       0.039619      0.018756   \n",
       "2007Q3  0.036748  0.020773  0.023338  ...       0.038682      0.028225   \n",
       "2007Q4  0.021745  0.005865  0.005081  ...       0.033362      0.009288   \n",
       "2008Q1  0.018386  0.009020 -0.005581  ...       0.028492      0.006137   \n",
       "\n",
       "        Singapore  Philippines  Indonesia  Malaysia  Thailand    Taiwan  \\\n",
       "time                                                                      \n",
       "1993Q1   0.087145    -0.004381   0.064024  0.085938  0.080000  0.064902   \n",
       "1993Q2   0.118075     0.016636   0.066068  0.131189  0.080000  0.065123   \n",
       "1993Q3   0.111130     0.031504   0.057959  0.109666  0.080000  0.067379   \n",
       "1993Q4   0.125324     0.034007   0.062365  0.075801  0.080000  0.069164   \n",
       "1994Q1   0.130709     0.049344   0.049743  0.049147  0.112509  0.069451   \n",
       "...           ...          ...        ...       ...       ...       ...   \n",
       "2007Q1   0.063092     0.065573   0.095297  0.039884  0.049846  0.041019   \n",
       "2007Q2   0.101377     0.081891   0.110900  0.080276  0.051197  0.051073   \n",
       "2007Q3   0.105562     0.064359   0.110100  0.093361  0.050126  0.066369   \n",
       "2007Q4   0.069739     0.065470   0.110700  0.151739  0.079587  0.062929   \n",
       "2008Q1   0.079339     0.047945   0.133400  0.167034  0.049144  0.058841   \n",
       "\n",
       "         China  predict_HongKong  \n",
       "time                              \n",
       "1993Q1  0.1430          0.055561  \n",
       "1993Q2  0.1410          0.067375  \n",
       "1993Q3  0.1350          0.062911  \n",
       "1993Q4  0.1350          0.060487  \n",
       "1994Q1  0.1250          0.069921  \n",
       "...        ...               ...  \n",
       "2007Q1  0.1110          0.046912  \n",
       "2007Q2  0.1167          0.057439  \n",
       "2007Q3  0.1002          0.062657  \n",
       "2007Q4  0.1017          0.074425  \n",
       "2008Q1  0.1238          0.072868  \n",
       "\n",
       "[61 rows x 27 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted = reg.predict(data[indep_var])\n",
    "data['predict_HongKong'] = predicted\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "注意上面我们为了预测，需要首先将原来的数据框变成与训练用的数据集相同的维度（和变量顺序），因为Scikit-Learn实际上把数据框当矩阵看待，所以不会自动匹配列标题。这点尤其需要注意。\n",
    "\n",
    "继而可以画图："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_54132/496109544.py:7: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  data['year'] = pd.to_datetime(data['time'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "\n",
    "sb.set()\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (15.0, 8.0)\n",
    "data['year'] = pd.to_datetime(data['time'])\n",
    "plt.plot(data['year'], data['HongKong'], c='r', lw=3, label='GDP growth')\n",
    "plt.plot(data['year'],\n",
    "         data['predict_HongKong'],\n",
    "         c='b',\n",
    "         lw=3,\n",
    "         label='Predicted GDP growth')\n",
    "plt.legend(loc='upper left', frameon=True)\n",
    "plt.axvline(pd.to_datetime('1997Q3'), c='c')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "以上我们选取了一些国家进行回归，问题是我们并不能保证如此处理是预测效果最好的（没有过拟合、欠拟合）。接下来我们展示在线性回归的基础上进行L1正则化，即Lasso回归的做法："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_54132/2270252052.py:2: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  data['quarter'] = pd.to_datetime(data['time'])\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Australia</th>\n",
       "      <th>Austria</th>\n",
       "      <th>Canada</th>\n",
       "      <th>Denmark</th>\n",
       "      <th>Finland</th>\n",
       "      <th>France</th>\n",
       "      <th>Germany</th>\n",
       "      <th>Italy</th>\n",
       "      <th>Japan</th>\n",
       "      <th>Korea</th>\n",
       "      <th>...</th>\n",
       "      <th>Norway</th>\n",
       "      <th>Switzerland</th>\n",
       "      <th>UnitedKingdom</th>\n",
       "      <th>UnitedStates</th>\n",
       "      <th>Singapore</th>\n",
       "      <th>Philippines</th>\n",
       "      <th>Indonesia</th>\n",
       "      <th>Malaysia</th>\n",
       "      <th>Thailand</th>\n",
       "      <th>Taiwan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.800000e+01</td>\n",
       "      <td>1.800000e+01</td>\n",
       "      <td>1.800000e+01</td>\n",
       "      <td>1.800000e+01</td>\n",
       "      <td>1.800000e+01</td>\n",
       "      <td>1.800000e+01</td>\n",
       "      <td>1.800000e+01</td>\n",
       "      <td>1.800000e+01</td>\n",
       "      <td>1.800000e+01</td>\n",
       "      <td>1.800000e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>1.800000e+01</td>\n",
       "      <td>1.800000e+01</td>\n",
       "      <td>1.800000e+01</td>\n",
       "      <td>1.800000e+01</td>\n",
       "      <td>1.800000e+01</td>\n",
       "      <td>1.800000e+01</td>\n",
       "      <td>1.800000e+01</td>\n",
       "      <td>1.800000e+01</td>\n",
       "      <td>1.800000e+01</td>\n",
       "      <td>1.800000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-1.480297e-16</td>\n",
       "      <td>8.635068e-17</td>\n",
       "      <td>5.859510e-17</td>\n",
       "      <td>4.317534e-17</td>\n",
       "      <td>-1.788693e-16</td>\n",
       "      <td>-6.167906e-18</td>\n",
       "      <td>5.396917e-17</td>\n",
       "      <td>-1.233581e-17</td>\n",
       "      <td>-8.172475e-17</td>\n",
       "      <td>-2.590520e-16</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.233581e-16</td>\n",
       "      <td>9.560254e-17</td>\n",
       "      <td>4.934325e-17</td>\n",
       "      <td>-1.480297e-16</td>\n",
       "      <td>-9.251859e-18</td>\n",
       "      <td>-2.590520e-16</td>\n",
       "      <td>7.524845e-16</td>\n",
       "      <td>2.097088e-16</td>\n",
       "      <td>1.727014e-16</td>\n",
       "      <td>-2.405483e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.855402e+00</td>\n",
       "      <td>-1.991950e+00</td>\n",
       "      <td>-1.269459e+00</td>\n",
       "      <td>-1.606998e+00</td>\n",
       "      <td>-1.777956e+00</td>\n",
       "      <td>-1.720618e+00</td>\n",
       "      <td>-1.726021e+00</td>\n",
       "      <td>-1.893443e+00</td>\n",
       "      <td>-2.034777e+00</td>\n",
       "      <td>-1.928518e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.860787e+00</td>\n",
       "      <td>-2.352864e+00</td>\n",
       "      <td>-2.062670e+00</td>\n",
       "      <td>-1.606066e+00</td>\n",
       "      <td>-1.926089e+00</td>\n",
       "      <td>-2.504369e+00</td>\n",
       "      <td>-1.578262e+00</td>\n",
       "      <td>-1.652365e+00</td>\n",
       "      <td>-2.216105e+00</td>\n",
       "      <td>-2.470933e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-4.605165e-01</td>\n",
       "      <td>-8.371103e-01</td>\n",
       "      <td>-6.916538e-01</td>\n",
       "      <td>-6.786943e-01</td>\n",
       "      <td>-5.799627e-01</td>\n",
       "      <td>-3.008681e-01</td>\n",
       "      <td>-5.469972e-01</td>\n",
       "      <td>-6.775107e-01</td>\n",
       "      <td>-7.353816e-01</td>\n",
       "      <td>-8.302294e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-6.995495e-01</td>\n",
       "      <td>-3.258248e-01</td>\n",
       "      <td>-2.696595e-01</td>\n",
       "      <td>-8.950738e-01</td>\n",
       "      <td>-4.866339e-01</td>\n",
       "      <td>-5.128031e-01</td>\n",
       "      <td>-5.376221e-01</td>\n",
       "      <td>-8.606132e-01</td>\n",
       "      <td>-4.255681e-01</td>\n",
       "      <td>-5.422058e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.912820e-01</td>\n",
       "      <td>3.180241e-01</td>\n",
       "      <td>-3.274983e-01</td>\n",
       "      <td>1.516707e-01</td>\n",
       "      <td>1.661822e-02</td>\n",
       "      <td>9.696599e-03</td>\n",
       "      <td>-7.079435e-02</td>\n",
       "      <td>2.097719e-01</td>\n",
       "      <td>2.978445e-01</td>\n",
       "      <td>7.503525e-02</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.155832e-02</td>\n",
       "      <td>-1.076865e-01</td>\n",
       "      <td>3.730978e-01</td>\n",
       "      <td>1.720636e-01</td>\n",
       "      <td>-1.043469e-01</td>\n",
       "      <td>1.074332e-01</td>\n",
       "      <td>-2.913644e-01</td>\n",
       "      <td>7.794584e-02</td>\n",
       "      <td>4.098353e-01</td>\n",
       "      <td>1.711136e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.567160e-01</td>\n",
       "      <td>7.405953e-01</td>\n",
       "      <td>6.225495e-01</td>\n",
       "      <td>7.017792e-01</td>\n",
       "      <td>6.247855e-01</td>\n",
       "      <td>7.063552e-01</td>\n",
       "      <td>8.586039e-01</td>\n",
       "      <td>7.976166e-01</td>\n",
       "      <td>7.879516e-01</td>\n",
       "      <td>3.921521e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>6.624267e-01</td>\n",
       "      <td>7.207502e-01</td>\n",
       "      <td>6.118767e-01</td>\n",
       "      <td>8.093997e-01</td>\n",
       "      <td>8.207957e-01</td>\n",
       "      <td>5.434178e-01</td>\n",
       "      <td>3.758168e-01</td>\n",
       "      <td>6.113059e-01</td>\n",
       "      <td>6.717231e-01</td>\n",
       "      <td>6.484883e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.531036e+00</td>\n",
       "      <td>1.241132e+00</td>\n",
       "      <td>1.991602e+00</td>\n",
       "      <td>1.770627e+00</td>\n",
       "      <td>1.432527e+00</td>\n",
       "      <td>1.537361e+00</td>\n",
       "      <td>1.353062e+00</td>\n",
       "      <td>1.194978e+00</td>\n",
       "      <td>1.236600e+00</td>\n",
       "      <td>1.801378e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>1.918698e+00</td>\n",
       "      <td>1.952238e+00</td>\n",
       "      <td>1.385831e+00</td>\n",
       "      <td>1.453857e+00</td>\n",
       "      <td>1.595478e+00</td>\n",
       "      <td>1.716803e+00</td>\n",
       "      <td>2.129883e+00</td>\n",
       "      <td>1.637198e+00</td>\n",
       "      <td>1.224307e+00</td>\n",
       "      <td>1.650306e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Australia       Austria        Canada       Denmark       Finland  \\\n",
       "count  1.800000e+01  1.800000e+01  1.800000e+01  1.800000e+01  1.800000e+01   \n",
       "mean  -1.480297e-16  8.635068e-17  5.859510e-17  4.317534e-17 -1.788693e-16   \n",
       "std    1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00   \n",
       "min   -1.855402e+00 -1.991950e+00 -1.269459e+00 -1.606998e+00 -1.777956e+00   \n",
       "25%   -4.605165e-01 -8.371103e-01 -6.916538e-01 -6.786943e-01 -5.799627e-01   \n",
       "50%    2.912820e-01  3.180241e-01 -3.274983e-01  1.516707e-01  1.661822e-02   \n",
       "75%    5.567160e-01  7.405953e-01  6.225495e-01  7.017792e-01  6.247855e-01   \n",
       "max    1.531036e+00  1.241132e+00  1.991602e+00  1.770627e+00  1.432527e+00   \n",
       "\n",
       "             France       Germany         Italy         Japan         Korea  \\\n",
       "count  1.800000e+01  1.800000e+01  1.800000e+01  1.800000e+01  1.800000e+01   \n",
       "mean  -6.167906e-18  5.396917e-17 -1.233581e-17 -8.172475e-17 -2.590520e-16   \n",
       "std    1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00   \n",
       "min   -1.720618e+00 -1.726021e+00 -1.893443e+00 -2.034777e+00 -1.928518e+00   \n",
       "25%   -3.008681e-01 -5.469972e-01 -6.775107e-01 -7.353816e-01 -8.302294e-01   \n",
       "50%    9.696599e-03 -7.079435e-02  2.097719e-01  2.978445e-01  7.503525e-02   \n",
       "75%    7.063552e-01  8.586039e-01  7.976166e-01  7.879516e-01  3.921521e-01   \n",
       "max    1.537361e+00  1.353062e+00  1.194978e+00  1.236600e+00  1.801378e+00   \n",
       "\n",
       "       ...        Norway   Switzerland  UnitedKingdom  UnitedStates  \\\n",
       "count  ...  1.800000e+01  1.800000e+01   1.800000e+01  1.800000e+01   \n",
       "mean   ... -1.233581e-16  9.560254e-17   4.934325e-17 -1.480297e-16   \n",
       "std    ...  1.000000e+00  1.000000e+00   1.000000e+00  1.000000e+00   \n",
       "min    ... -1.860787e+00 -2.352864e+00  -2.062670e+00 -1.606066e+00   \n",
       "25%    ... -6.995495e-01 -3.258248e-01  -2.696595e-01 -8.950738e-01   \n",
       "50%    ... -2.155832e-02 -1.076865e-01   3.730978e-01  1.720636e-01   \n",
       "75%    ...  6.624267e-01  7.207502e-01   6.118767e-01  8.093997e-01   \n",
       "max    ...  1.918698e+00  1.952238e+00   1.385831e+00  1.453857e+00   \n",
       "\n",
       "          Singapore   Philippines     Indonesia      Malaysia      Thailand  \\\n",
       "count  1.800000e+01  1.800000e+01  1.800000e+01  1.800000e+01  1.800000e+01   \n",
       "mean  -9.251859e-18 -2.590520e-16  7.524845e-16  2.097088e-16  1.727014e-16   \n",
       "std    1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00   \n",
       "min   -1.926089e+00 -2.504369e+00 -1.578262e+00 -1.652365e+00 -2.216105e+00   \n",
       "25%   -4.866339e-01 -5.128031e-01 -5.376221e-01 -8.606132e-01 -4.255681e-01   \n",
       "50%   -1.043469e-01  1.074332e-01 -2.913644e-01  7.794584e-02  4.098353e-01   \n",
       "75%    8.207957e-01  5.434178e-01  3.758168e-01  6.113059e-01  6.717231e-01   \n",
       "max    1.595478e+00  1.716803e+00  2.129883e+00  1.637198e+00  1.224307e+00   \n",
       "\n",
       "             Taiwan  \n",
       "count  1.800000e+01  \n",
       "mean  -2.405483e-16  \n",
       "std    1.000000e+00  \n",
       "min   -2.470933e+00  \n",
       "25%   -5.422058e-01  \n",
       "50%    1.711136e-01  \n",
       "75%    6.484883e-01  \n",
       "max    1.650306e+00  \n",
       "\n",
       "[8 rows x 23 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('csv/hcw.csv')\n",
    "data['quarter'] = pd.to_datetime(data['time'])\n",
    "data = data.set_index('quarter')\n",
    "data.index = data.index.to_period('Q')\n",
    "pre1997 = data[data.index <= '1997Q2']\n",
    "## 准备X,y\n",
    "y = pre1997['HongKong']\n",
    "X = pre1997.drop(['HongKong', 'China', 'time'], axis=1)\n",
    "std_X = (X - X.mean()\n",
    "         ) / X.std()  # 标准化X，如果下面的linear.model.Lasso不设置normalize=True的话，需要手动标准化\n",
    "std_X.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.00384836, -0.        ,  0.00106811,  0.        ,  0.        ,\n",
       "        0.        ,  0.        , -0.        , -0.00839452, -0.00936549,\n",
       "        0.00102252,  0.        , -0.        , -0.        ,  0.        ,\n",
       "        0.        ,  0.00012505,  0.        , -0.00051634,  0.        ,\n",
       "       -0.00216037, -0.        ,  0.00594295])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 回归\n",
    "lasso = linear_model.Lasso(alpha=0.0005)\n",
    "lasso.fit(std_X, y)  ## 训练模型\n",
    "lasso.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "选项中的alpha即我们上面提到的$\\lambda$，而normalize选项设定为需要进行标准化。可以看到上面很多系数的取值都为0，达到了变量选择的目的。\n",
    "\n",
    "然而上面的alpha是我们设定的，一个更好的办法是通过交叉验证选取合适的alpha。可以使用LassoCV来做："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/Anaconda/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.715e-07, tolerance: 6.232e-07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/Anaconda/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.393e-07, tolerance: 6.232e-07\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "选择的alpha= 0.0006731855207581835\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 0.0038306 , -0.        ,  0.00030148,  0.        , -0.        ,\n",
       "        0.        ,  0.        , -0.        , -0.00826843, -0.00891092,\n",
       "        0.00145376, -0.        , -0.        , -0.        ,  0.        ,\n",
       "       -0.        ,  0.        ,  0.        , -0.        ,  0.        ,\n",
       "       -0.00204314, -0.        ,  0.00594418])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso_cv = linear_model.LassoCV(eps=1e-5, cv=10,\n",
    "                                max_iter=1000)  #10折交叉验证，最大迭代次数1000次\n",
    "lasso_cv.fit(std_X, y)\n",
    "print(\"选择的alpha=\", lasso_cv.alpha_)\n",
    "lasso_cv.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以使用路径图来查看随着alpha的变动，系数的变动情况："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Coefficient Paths')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import lasso_path\n",
    "\n",
    "alphas_lasso, coefs_lasso, _ = lasso_path(std_X, y, eps=1e-5)\n",
    "neg_log_alphas_lasso = -np.log10(alphas_lasso)\n",
    "for coef_l in coefs_lasso:\n",
    "    l1 = plt.plot(neg_log_alphas_lasso, coef_l)\n",
    "\n",
    "plt.xlabel('-Log(alpha)')\n",
    "plt.ylabel('coefficients')\n",
    "plt.title('Coefficient Paths')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "进一步做预测："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_54132/347982631.py:12: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  data['year'] = pd.to_datetime(data['time'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred_set = data.drop(['HongKong', 'China', 'time'], axis=1)\n",
    "pred_set = (pred_set -\n",
    "            X.mean()) / X.std()  ## 因为Lasso命令之前的步骤中对X进行了标准化，所以需要对预测集也进行相同的标准化操作\n",
    "predicted = lasso_cv.predict(pred_set)\n",
    "data['predict_HongKong'] = predicted\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "\n",
    "sb.set()\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (15.0, 8.0)\n",
    "data['year'] = pd.to_datetime(data['time'])\n",
    "plt.plot(data['year'], data['HongKong'], c='r', lw=3, label='GDP growth')\n",
    "plt.plot(data['year'],\n",
    "         data['predict_HongKong'],\n",
    "         c='b',\n",
    "         lw=3,\n",
    "         label='Predicted GDP growth')\n",
    "plt.legend(loc='upper left', frameon=True)\n",
    "plt.axvline(pd.to_datetime('1997Q3'), c='c')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "此外，还有一个比较常见的做法是单纯使用Lasso作为变量选择工具，使用Lasso选取变量后，继续使用OLS进行估计和预测，这杯成为『Post-estimation』，我们可以如此进行此操作："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "选取的国家地区：\n",
      " Index(['Australia', 'Canada', 'Japan', 'Korea', 'Mexico', 'Malaysia',\n",
      "       'Taiwan'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Australia</th>\n",
       "      <th>Canada</th>\n",
       "      <th>Japan</th>\n",
       "      <th>Korea</th>\n",
       "      <th>Mexico</th>\n",
       "      <th>Malaysia</th>\n",
       "      <th>Taiwan</th>\n",
       "      <th>HongKong</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quarter</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1993Q1</th>\n",
       "      <td>0.040489</td>\n",
       "      <td>0.010064</td>\n",
       "      <td>0.012683</td>\n",
       "      <td>0.058586</td>\n",
       "      <td>0.043746</td>\n",
       "      <td>0.085938</td>\n",
       "      <td>0.064902</td>\n",
       "      <td>0.062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993Q2</th>\n",
       "      <td>0.037857</td>\n",
       "      <td>0.021264</td>\n",
       "      <td>-0.005571</td>\n",
       "      <td>0.069521</td>\n",
       "      <td>0.012290</td>\n",
       "      <td>0.131189</td>\n",
       "      <td>0.065123</td>\n",
       "      <td>0.059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993Q3</th>\n",
       "      <td>0.022509</td>\n",
       "      <td>0.018919</td>\n",
       "      <td>-0.017558</td>\n",
       "      <td>0.081646</td>\n",
       "      <td>0.004462</td>\n",
       "      <td>0.109666</td>\n",
       "      <td>0.067379</td>\n",
       "      <td>0.058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993Q4</th>\n",
       "      <td>0.028747</td>\n",
       "      <td>0.025317</td>\n",
       "      <td>-0.010101</td>\n",
       "      <td>0.085533</td>\n",
       "      <td>0.015493</td>\n",
       "      <td>0.075801</td>\n",
       "      <td>0.069164</td>\n",
       "      <td>0.062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994Q1</th>\n",
       "      <td>0.033990</td>\n",
       "      <td>0.043567</td>\n",
       "      <td>-0.022503</td>\n",
       "      <td>0.085922</td>\n",
       "      <td>0.034448</td>\n",
       "      <td>0.049147</td>\n",
       "      <td>0.069451</td>\n",
       "      <td>0.079</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Australia    Canada     Japan     Korea    Mexico  Malaysia  \\\n",
       "quarter                                                                \n",
       "1993Q1    0.040489  0.010064  0.012683  0.058586  0.043746  0.085938   \n",
       "1993Q2    0.037857  0.021264 -0.005571  0.069521  0.012290  0.131189   \n",
       "1993Q3    0.022509  0.018919 -0.017558  0.081646  0.004462  0.109666   \n",
       "1993Q4    0.028747  0.025317 -0.010101  0.085533  0.015493  0.075801   \n",
       "1994Q1    0.033990  0.043567 -0.022503  0.085922  0.034448  0.049147   \n",
       "\n",
       "           Taiwan  HongKong  \n",
       "quarter                      \n",
       "1993Q1   0.064902     0.062  \n",
       "1993Q2   0.065123     0.059  \n",
       "1993Q3   0.067379     0.058  \n",
       "1993Q4   0.069164     0.062  \n",
       "1994Q1   0.069451     0.079  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_vars = X.columns[lasso_cv.coef_ != 0]\n",
    "print(\"选取的国家地区：\\n\", selected_vars)\n",
    "data_ols = data.loc[:, selected_vars]\n",
    "data_ols['HongKong'] = data[\"HongKong\"]\n",
    "data_ols.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Australia</th>\n",
       "      <th>Canada</th>\n",
       "      <th>Japan</th>\n",
       "      <th>Korea</th>\n",
       "      <th>Mexico</th>\n",
       "      <th>Malaysia</th>\n",
       "      <th>Taiwan</th>\n",
       "      <th>HongKong</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quarter</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1993Q1</th>\n",
       "      <td>0.040489</td>\n",
       "      <td>0.010064</td>\n",
       "      <td>0.012683</td>\n",
       "      <td>0.058586</td>\n",
       "      <td>0.043746</td>\n",
       "      <td>0.085938</td>\n",
       "      <td>0.064902</td>\n",
       "      <td>0.062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993Q2</th>\n",
       "      <td>0.037857</td>\n",
       "      <td>0.021264</td>\n",
       "      <td>-0.005571</td>\n",
       "      <td>0.069521</td>\n",
       "      <td>0.012290</td>\n",
       "      <td>0.131189</td>\n",
       "      <td>0.065123</td>\n",
       "      <td>0.059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993Q3</th>\n",
       "      <td>0.022509</td>\n",
       "      <td>0.018919</td>\n",
       "      <td>-0.017558</td>\n",
       "      <td>0.081646</td>\n",
       "      <td>0.004462</td>\n",
       "      <td>0.109666</td>\n",
       "      <td>0.067379</td>\n",
       "      <td>0.058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993Q4</th>\n",
       "      <td>0.028747</td>\n",
       "      <td>0.025317</td>\n",
       "      <td>-0.010101</td>\n",
       "      <td>0.085533</td>\n",
       "      <td>0.015493</td>\n",
       "      <td>0.075801</td>\n",
       "      <td>0.069164</td>\n",
       "      <td>0.062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994Q1</th>\n",
       "      <td>0.033990</td>\n",
       "      <td>0.043567</td>\n",
       "      <td>-0.022503</td>\n",
       "      <td>0.085922</td>\n",
       "      <td>0.034448</td>\n",
       "      <td>0.049147</td>\n",
       "      <td>0.069451</td>\n",
       "      <td>0.079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994Q2</th>\n",
       "      <td>0.037919</td>\n",
       "      <td>0.050225</td>\n",
       "      <td>-0.005157</td>\n",
       "      <td>0.088415</td>\n",
       "      <td>0.063911</td>\n",
       "      <td>0.061173</td>\n",
       "      <td>0.070135</td>\n",
       "      <td>0.068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994Q3</th>\n",
       "      <td>0.052289</td>\n",
       "      <td>0.065122</td>\n",
       "      <td>0.014087</td>\n",
       "      <td>0.092796</td>\n",
       "      <td>0.064322</td>\n",
       "      <td>0.101110</td>\n",
       "      <td>0.069298</td>\n",
       "      <td>0.046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994Q4</th>\n",
       "      <td>0.031071</td>\n",
       "      <td>0.067331</td>\n",
       "      <td>0.005427</td>\n",
       "      <td>0.117461</td>\n",
       "      <td>0.066942</td>\n",
       "      <td>0.134978</td>\n",
       "      <td>0.076049</td>\n",
       "      <td>0.052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995Q1</th>\n",
       "      <td>0.008696</td>\n",
       "      <td>0.050921</td>\n",
       "      <td>0.003919</td>\n",
       "      <td>0.115388</td>\n",
       "      <td>0.043418</td>\n",
       "      <td>0.130363</td>\n",
       "      <td>0.071104</td>\n",
       "      <td>0.037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995Q2</th>\n",
       "      <td>0.006774</td>\n",
       "      <td>0.031525</td>\n",
       "      <td>0.015349</td>\n",
       "      <td>0.121889</td>\n",
       "      <td>-0.059943</td>\n",
       "      <td>0.119140</td>\n",
       "      <td>0.069214</td>\n",
       "      <td>0.029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995Q3</th>\n",
       "      <td>0.003028</td>\n",
       "      <td>0.018180</td>\n",
       "      <td>0.015939</td>\n",
       "      <td>0.132274</td>\n",
       "      <td>-0.079015</td>\n",
       "      <td>0.065085</td>\n",
       "      <td>0.063417</td>\n",
       "      <td>0.012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995Q4</th>\n",
       "      <td>0.010982</td>\n",
       "      <td>0.015166</td>\n",
       "      <td>0.026233</td>\n",
       "      <td>0.092351</td>\n",
       "      <td>-0.067619</td>\n",
       "      <td>0.074479</td>\n",
       "      <td>0.048752</td>\n",
       "      <td>0.015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996Q1</th>\n",
       "      <td>0.038182</td>\n",
       "      <td>0.007821</td>\n",
       "      <td>0.026878</td>\n",
       "      <td>0.094772</td>\n",
       "      <td>-0.055922</td>\n",
       "      <td>0.103842</td>\n",
       "      <td>0.054664</td>\n",
       "      <td>0.025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996Q2</th>\n",
       "      <td>0.034520</td>\n",
       "      <td>0.011510</td>\n",
       "      <td>0.022471</td>\n",
       "      <td>0.072358</td>\n",
       "      <td>0.022377</td>\n",
       "      <td>0.092732</td>\n",
       "      <td>0.059415</td>\n",
       "      <td>0.036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996Q3</th>\n",
       "      <td>0.036673</td>\n",
       "      <td>0.021660</td>\n",
       "      <td>0.012733</td>\n",
       "      <td>0.055408</td>\n",
       "      <td>0.054119</td>\n",
       "      <td>0.096904</td>\n",
       "      <td>0.059393</td>\n",
       "      <td>0.047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996Q4</th>\n",
       "      <td>0.038987</td>\n",
       "      <td>0.024704</td>\n",
       "      <td>0.021495</td>\n",
       "      <td>0.057294</td>\n",
       "      <td>0.055560</td>\n",
       "      <td>0.095856</td>\n",
       "      <td>0.070151</td>\n",
       "      <td>0.059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997Q1</th>\n",
       "      <td>0.025748</td>\n",
       "      <td>0.035775</td>\n",
       "      <td>0.025330</td>\n",
       "      <td>0.031708</td>\n",
       "      <td>0.028028</td>\n",
       "      <td>0.068248</td>\n",
       "      <td>0.063613</td>\n",
       "      <td>0.058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997Q2</th>\n",
       "      <td>0.051863</td>\n",
       "      <td>0.038688</td>\n",
       "      <td>0.006164</td>\n",
       "      <td>0.053364</td>\n",
       "      <td>0.056595</td>\n",
       "      <td>0.065035</td>\n",
       "      <td>0.060897</td>\n",
       "      <td>0.072</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Australia    Canada     Japan     Korea    Mexico  Malaysia  \\\n",
       "quarter                                                                \n",
       "1993Q1    0.040489  0.010064  0.012683  0.058586  0.043746  0.085938   \n",
       "1993Q2    0.037857  0.021264 -0.005571  0.069521  0.012290  0.131189   \n",
       "1993Q3    0.022509  0.018919 -0.017558  0.081646  0.004462  0.109666   \n",
       "1993Q4    0.028747  0.025317 -0.010101  0.085533  0.015493  0.075801   \n",
       "1994Q1    0.033990  0.043567 -0.022503  0.085922  0.034448  0.049147   \n",
       "1994Q2    0.037919  0.050225 -0.005157  0.088415  0.063911  0.061173   \n",
       "1994Q3    0.052289  0.065122  0.014087  0.092796  0.064322  0.101110   \n",
       "1994Q4    0.031071  0.067331  0.005427  0.117461  0.066942  0.134978   \n",
       "1995Q1    0.008696  0.050921  0.003919  0.115388  0.043418  0.130363   \n",
       "1995Q2    0.006774  0.031525  0.015349  0.121889 -0.059943  0.119140   \n",
       "1995Q3    0.003028  0.018180  0.015939  0.132274 -0.079015  0.065085   \n",
       "1995Q4    0.010982  0.015166  0.026233  0.092351 -0.067619  0.074479   \n",
       "1996Q1    0.038182  0.007821  0.026878  0.094772 -0.055922  0.103842   \n",
       "1996Q2    0.034520  0.011510  0.022471  0.072358  0.022377  0.092732   \n",
       "1996Q3    0.036673  0.021660  0.012733  0.055408  0.054119  0.096904   \n",
       "1996Q4    0.038987  0.024704  0.021495  0.057294  0.055560  0.095856   \n",
       "1997Q1    0.025748  0.035775  0.025330  0.031708  0.028028  0.068248   \n",
       "1997Q2    0.051863  0.038688  0.006164  0.053364  0.056595  0.065035   \n",
       "\n",
       "           Taiwan  HongKong  \n",
       "quarter                      \n",
       "1993Q1   0.064902     0.062  \n",
       "1993Q2   0.065123     0.059  \n",
       "1993Q3   0.067379     0.058  \n",
       "1993Q4   0.069164     0.062  \n",
       "1994Q1   0.069451     0.079  \n",
       "1994Q2   0.070135     0.068  \n",
       "1994Q3   0.069298     0.046  \n",
       "1994Q4   0.076049     0.052  \n",
       "1995Q1   0.071104     0.037  \n",
       "1995Q2   0.069214     0.029  \n",
       "1995Q3   0.063417     0.012  \n",
       "1995Q4   0.048752     0.015  \n",
       "1996Q1   0.054664     0.025  \n",
       "1996Q2   0.059415     0.036  \n",
       "1996Q3   0.059393     0.047  \n",
       "1996Q4   0.070151     0.059  \n",
       "1997Q1   0.063613     0.058  \n",
       "1997Q2   0.060897     0.072  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pre1997 = data_ols[data.index <= '1997Q2']\n",
    "pre1997"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Australia</th>\n",
       "      <th>Canada</th>\n",
       "      <th>Japan</th>\n",
       "      <th>Korea</th>\n",
       "      <th>Mexico</th>\n",
       "      <th>Malaysia</th>\n",
       "      <th>Taiwan</th>\n",
       "      <th>HongKong</th>\n",
       "      <th>predict_HongKong</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quarter</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1993Q1</th>\n",
       "      <td>0.040489</td>\n",
       "      <td>0.010064</td>\n",
       "      <td>0.012683</td>\n",
       "      <td>0.058586</td>\n",
       "      <td>0.043746</td>\n",
       "      <td>0.085938</td>\n",
       "      <td>0.064902</td>\n",
       "      <td>0.062</td>\n",
       "      <td>0.057415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993Q2</th>\n",
       "      <td>0.037857</td>\n",
       "      <td>0.021264</td>\n",
       "      <td>-0.005571</td>\n",
       "      <td>0.069521</td>\n",
       "      <td>0.012290</td>\n",
       "      <td>0.131189</td>\n",
       "      <td>0.065123</td>\n",
       "      <td>0.059</td>\n",
       "      <td>0.060084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993Q3</th>\n",
       "      <td>0.022509</td>\n",
       "      <td>0.018919</td>\n",
       "      <td>-0.017558</td>\n",
       "      <td>0.081646</td>\n",
       "      <td>0.004462</td>\n",
       "      <td>0.109666</td>\n",
       "      <td>0.067379</td>\n",
       "      <td>0.058</td>\n",
       "      <td>0.061823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993Q4</th>\n",
       "      <td>0.028747</td>\n",
       "      <td>0.025317</td>\n",
       "      <td>-0.010101</td>\n",
       "      <td>0.085533</td>\n",
       "      <td>0.015493</td>\n",
       "      <td>0.075801</td>\n",
       "      <td>0.069164</td>\n",
       "      <td>0.062</td>\n",
       "      <td>0.063590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994Q1</th>\n",
       "      <td>0.033990</td>\n",
       "      <td>0.043567</td>\n",
       "      <td>-0.022503</td>\n",
       "      <td>0.085922</td>\n",
       "      <td>0.034448</td>\n",
       "      <td>0.049147</td>\n",
       "      <td>0.069451</td>\n",
       "      <td>0.079</td>\n",
       "      <td>0.076519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007Q1</th>\n",
       "      <td>0.058013</td>\n",
       "      <td>0.030712</td>\n",
       "      <td>0.023959</td>\n",
       "      <td>0.030068</td>\n",
       "      <td>0.038222</td>\n",
       "      <td>0.039884</td>\n",
       "      <td>0.041019</td>\n",
       "      <td>0.055</td>\n",
       "      <td>0.048178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007Q2</th>\n",
       "      <td>0.059519</td>\n",
       "      <td>0.039827</td>\n",
       "      <td>0.014193</td>\n",
       "      <td>0.037898</td>\n",
       "      <td>0.022226</td>\n",
       "      <td>0.080276</td>\n",
       "      <td>0.051073</td>\n",
       "      <td>0.062</td>\n",
       "      <td>0.059301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007Q3</th>\n",
       "      <td>0.056649</td>\n",
       "      <td>0.034742</td>\n",
       "      <td>0.012907</td>\n",
       "      <td>0.039380</td>\n",
       "      <td>0.031162</td>\n",
       "      <td>0.093361</td>\n",
       "      <td>0.066369</td>\n",
       "      <td>0.068</td>\n",
       "      <td>0.073418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007Q4</th>\n",
       "      <td>0.045825</td>\n",
       "      <td>0.038128</td>\n",
       "      <td>-0.004768</td>\n",
       "      <td>0.035359</td>\n",
       "      <td>0.062714</td>\n",
       "      <td>0.151739</td>\n",
       "      <td>0.062929</td>\n",
       "      <td>0.069</td>\n",
       "      <td>0.072205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008Q1</th>\n",
       "      <td>0.027523</td>\n",
       "      <td>0.029217</td>\n",
       "      <td>-0.013647</td>\n",
       "      <td>0.028462</td>\n",
       "      <td>0.056493</td>\n",
       "      <td>0.167034</td>\n",
       "      <td>0.058841</td>\n",
       "      <td>0.073</td>\n",
       "      <td>0.067706</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Australia    Canada     Japan     Korea    Mexico  Malaysia  \\\n",
       "quarter                                                                \n",
       "1993Q1    0.040489  0.010064  0.012683  0.058586  0.043746  0.085938   \n",
       "1993Q2    0.037857  0.021264 -0.005571  0.069521  0.012290  0.131189   \n",
       "1993Q3    0.022509  0.018919 -0.017558  0.081646  0.004462  0.109666   \n",
       "1993Q4    0.028747  0.025317 -0.010101  0.085533  0.015493  0.075801   \n",
       "1994Q1    0.033990  0.043567 -0.022503  0.085922  0.034448  0.049147   \n",
       "...            ...       ...       ...       ...       ...       ...   \n",
       "2007Q1    0.058013  0.030712  0.023959  0.030068  0.038222  0.039884   \n",
       "2007Q2    0.059519  0.039827  0.014193  0.037898  0.022226  0.080276   \n",
       "2007Q3    0.056649  0.034742  0.012907  0.039380  0.031162  0.093361   \n",
       "2007Q4    0.045825  0.038128 -0.004768  0.035359  0.062714  0.151739   \n",
       "2008Q1    0.027523  0.029217 -0.013647  0.028462  0.056493  0.167034   \n",
       "\n",
       "           Taiwan  HongKong  predict_HongKong  \n",
       "quarter                                        \n",
       "1993Q1   0.064902     0.062          0.057415  \n",
       "1993Q2   0.065123     0.059          0.060084  \n",
       "1993Q3   0.067379     0.058          0.061823  \n",
       "1993Q4   0.069164     0.062          0.063590  \n",
       "1994Q1   0.069451     0.079          0.076519  \n",
       "...           ...       ...               ...  \n",
       "2007Q1   0.041019     0.055          0.048178  \n",
       "2007Q2   0.051073     0.062          0.059301  \n",
       "2007Q3   0.066369     0.068          0.073418  \n",
       "2007Q4   0.062929     0.069          0.072205  \n",
       "2008Q1   0.058841     0.073          0.067706  \n",
       "\n",
       "[61 rows x 9 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = pre1997['HongKong']\n",
    "X = pre1997.drop(\"HongKong\", axis=1)\n",
    "reg = linear_model.LinearRegression().fit(X, y)  ## 训练模型\n",
    "predicted = reg.predict(data_ols.drop(\"HongKong\", axis=1))\n",
    "data_ols['predict_HongKong'] = predicted\n",
    "data_ols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_54132/2598912699.py:3: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  data_ols['year'] = pd.to_datetime(data['time'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (15.0, 8.0)\n",
    "data_ols['year'] = pd.to_datetime(data['time'])\n",
    "plt.plot(data_ols['year'],\n",
    "         data_ols['HongKong'],\n",
    "         c='r',\n",
    "         lw=3,\n",
    "         label='GDP growth')\n",
    "plt.plot(data_ols['year'],\n",
    "         data_ols['predict_HongKong'],\n",
    "         c='b',\n",
    "         lw=3,\n",
    "         label='Predicted GDP growth')\n",
    "plt.legend(loc='upper left', frameon=True)\n",
    "plt.axvline(pd.to_datetime('1997Q3'), c='c')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Anaconda",
   "language": "python",
   "name": "anaconda"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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