Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

remove deprecated Solution code, use HARKobject in the codebase #772

Merged
merged 4 commits into from
Aug 7, 2020
Merged

remove deprecated Solution code, use HARKobject in the codebase #772

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
4 commits
Select commit Hold shift + click to select a range
343ee9e
remove deprecated Solution code, use HARKobject in the codebase
MridulS Jul 23, 2020
4086691
Merge branch 'master' into remove_Solution_class
Jul 23, 2020
55ace37
Merge branch 'master' into remove_Solution_class
llorracc Jul 30, 2020
bcbd519
Merge branch 'master' into remove_Solution_class
Aug 6, 2020
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
121 changes: 61 additions & 60 deletions HARK/ConsumptionSaving/ConsIndShockModel.py
View file
Open in desktop

Large diffs are not rendered by default.

132 changes: 66 additions & 66 deletions HARK/ConsumptionSaving/ConsLaborModel.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -12,7 +12,7 @@

from copy import copy
import numpy as np
from HARK.core import Solution
from HARK.core import HARKobject
from HARK.utilities import CRRAutilityP, CRRAutilityP_inv
from HARK.interpolation import (
LinearInterp,
Expand All @@ -30,7 +30,7 @@
import matplotlib.pyplot as plt


class ConsumerLaborSolution(Solution):
class ConsumerLaborSolution(HARKobject):
"""
A class for representing one period of the solution to a Consumer Labor problem.
"""
Expand All @@ -40,7 +40,7 @@ class ConsumerLaborSolution(Solution):
def __init__(self, cFunc=None, LbrFunc=None, vFunc=None, vPfunc=None, bNrmMin=None):
"""
The constructor for a new ConsumerSolution object.

Parameters
----------
cFunc : function
Expand All @@ -58,7 +58,7 @@ def __init__(self, cFunc=None, LbrFunc=None, vFunc=None, vPfunc=None, bNrmMin=No
bNrmMin: float
The minimum allowable bank balances for this period, as a function of
the transitory shock. cFunc, LbrFunc, etc are undefined for bNrm < bNrmMin(TranShk).

Returns
-------
None
Expand Down Expand Up @@ -97,27 +97,27 @@ def solveConsLaborIntMarg(
on the intensive margin by using the endogenous grid method to invert the first
order conditions for optimal composite consumption and between consumption and
leisure, obviating any search for optimal controls.
Parameters

Parameters
----------
solution_next : ConsumerLaborSolution
The solution to the next period's problem; must have the attributes
vPfunc and bNrmMinFunc representing marginal value of bank balances and
minimum (normalized) bank balances as a function of the transitory shock.
PermShkDstn: [np.array]
Discrete distribution of permanent productivity shocks.
Discrete distribution of permanent productivity shocks.
TranShkDstn: [np.array]
Discrete distribution of transitory productivity shocks.
Discrete distribution of transitory productivity shocks.
LivPrb : float
Survival probability; likelihood of being alive at the beginning of
the succeeding period.
the succeeding period.
DiscFac : float
Intertemporal discount factor.
CRRA : float
Coefficient of relative risk aversion over the composite good.
Coefficient of relative risk aversion over the composite good.
Rfree : float
Risk free interest rate on assets retained at the end of the period.
PermGroFac : float
PermGroFac : float
Expected permanent income growth factor for next period.
BoroCnstArt: float or None
Borrowing constraint for the minimum allowable assets to end the
Expand All @@ -138,7 +138,7 @@ def solveConsLaborIntMarg(
LbrCost: float
Cost parameter for supplying labor: u_t = U(x_t), x_t = c_t*z_t^LbrCost,
where z_t is leisure = 1 - Lbr_t.

Returns
-------
solution_now : ConsumerLaborSolution
Expand Down Expand Up @@ -176,55 +176,55 @@ def solveConsLaborIntMarg(
# Make tiled versions of the grid of a_t values and the components of the shock distribution
aXtraCount = aXtraGrid.size
bNrmGrid = aXtraGrid # Next period's bank balances before labor income

# Replicated axtraGrid of b_t values (bNowGrid) for each transitory (productivity) shock
bNrmGrid_rep = np.tile(np.reshape(bNrmGrid, (aXtraCount, 1)), (1, TranShkCount))
bNrmGrid_rep = np.tile(np.reshape(bNrmGrid, (aXtraCount, 1)), (1, TranShkCount))

# Replicated transitory shock values for each a_t state
TranShkVals_rep = np.tile(np.reshape(TranShkVals, (1, TranShkCount)), (aXtraCount, 1))

# Replicated transitory shock probabilities for each a_t state
TranShkPrbs_rep = np.tile(np.reshape(TranShkPrbs, (1, TranShkCount)), (aXtraCount, 1))

# Construct a function that gives marginal value of next period's bank balances *just before* the transitory shock arrives
# Next period's marginal value at every transitory shock and every bank balances gridpoint
vPNext = vPfunc_next(bNrmGrid_rep, TranShkVals_rep)

# Integrate out the transitory shocks (in TranShkVals direction) to get expected vP just before the transitory shock
vPbarNext = np.sum(vPNext * TranShkPrbs_rep, axis=1)
vPbarNext = np.sum(vPNext * TranShkPrbs_rep, axis=1)

# Transformed marginal value through the inverse marginal utility function to "decurve" it
vPbarNvrsNext = uPinv(vPbarNext)
vPbarNvrsNext = uPinv(vPbarNext)

# Linear interpolation over b_{t+1}, adding a point at minimal value of b = 0.
vPbarNvrsFuncNext = LinearInterp(np.insert(bNrmGrid, 0, 0.0), np.insert(vPbarNvrsNext, 0, 0.0))
vPbarNvrsFuncNext = LinearInterp(np.insert(bNrmGrid, 0, 0.0), np.insert(vPbarNvrsNext, 0, 0.0))

# "Recurve" the intermediate marginal value function through the marginal utility function
vPbarFuncNext = MargValueFunc(vPbarNvrsFuncNext, CRRA)
vPbarFuncNext = MargValueFunc(vPbarNvrsFuncNext, CRRA)

# Get next period's bank balances at each permanent shock from each end-of-period asset values
# Replicated grid of a_t values for each permanent (productivity) shock
aNrmGrid_rep = np.tile(np.reshape(aXtraGrid, (aXtraCount, 1)), (1, PermShkCount))
aNrmGrid_rep = np.tile(np.reshape(aXtraGrid, (aXtraCount, 1)), (1, PermShkCount))

# Replicated permanent shock values for each a_t value
PermShkVals_rep = np.tile(np.reshape(PermShkVals, (1, PermShkCount)), (aXtraCount, 1))
PermShkVals_rep = np.tile(np.reshape(PermShkVals, (1, PermShkCount)), (aXtraCount, 1))

# Replicated permanent shock probabilities for each a_t value
PermShkPrbs_rep = np.tile(np.reshape(PermShkPrbs, (1, PermShkCount)), (aXtraCount, 1))
PermShkPrbs_rep = np.tile(np.reshape(PermShkPrbs, (1, PermShkCount)), (aXtraCount, 1))
bNrmNext = (Rfree / (PermGroFac * PermShkVals_rep)) * aNrmGrid_rep

# Calculate marginal value of end-of-period assets at each a_t gridpoint
# Get marginal value of bank balances next period at each shock
vPbarNext = (PermGroFac * PermShkVals_rep) ** (-CRRA) * vPbarFuncNext(bNrmNext)

# Take expectation across permanent income shocks
EndOfPrdvP = (DiscFac * Rfree * LivPrb * np.sum(vPbarNext * PermShkPrbs_rep,
EndOfPrdvP = (DiscFac * Rfree * LivPrb * np.sum(vPbarNext * PermShkPrbs_rep,
axis=1, keepdims=True))

# Compute scaling factor for each transitory shock
TranShkScaleFac_temp = (frac * (WageRte * TranShkGrid) ** (LbrCost * frac)
* (LbrCost ** (-LbrCost * frac) + LbrCost ** frac ))

# Flip it to be a row vector
TranShkScaleFac = np.reshape(TranShkScaleFac_temp, (1, TranShkGrid.size))

Expand All @@ -234,12 +234,12 @@ def solveConsLaborIntMarg(
# Transform the composite good x_t values into consumption c_t and leisure z_t values
TranShkGrid_rep = np.tile(np.reshape(TranShkGrid, (1, TranShkGrid.size)), (aXtraCount, 1))
xNowPow = xNow ** frac # Will use this object multiple times in math below

# Find optimal consumption from optimal composite good
cNrmNow = (((WageRte * TranShkGrid_rep) / LbrCost) ** (LbrCost * frac)) * xNowPow

# Find optimal leisure from optimal composite good
LsrNow = (LbrCost / (WageRte * TranShkGrid_rep)) ** frac * xNowPow
LsrNow = (LbrCost / (WageRte * TranShkGrid_rep)) ** frac * xNowPow

# The zero-th transitory shock is TranShk=0, and the solution is to not work: Lsr = 1, Lbr = 0.
cNrmNow[:, 0] = uPinv(EndOfPrdvP.flatten())
Expand All @@ -248,7 +248,7 @@ def solveConsLaborIntMarg(
# Agent cannot choose to work a negative amount of time. When this occurs, set
# leisure to one and recompute consumption using simplified first order condition.
# Find where labor would be negative if unconstrained
violates_labor_constraint = (LsrNow > 1.0)
violates_labor_constraint = (LsrNow > 1.0)
EndOfPrdvP_temp = np.tile(np.reshape(EndOfPrdvP, (aXtraCount, 1)), (1, TranShkCount))
cNrmNow[violates_labor_constraint] = uPinv(EndOfPrdvP_temp[violates_labor_constraint])
LsrNow[violates_labor_constraint] = 1.0 # Set up z=1, upper limit
Expand All @@ -265,7 +265,7 @@ def solveConsLaborIntMarg(
# Consume nothing
cNowArray = np.concatenate((np.zeros((1, TranShkGrid.size)), cNrmNow), axis=0)
# And no leisure!
LsrNowArray = np.concatenate((np.zeros((1, TranShkGrid.size)), LsrNow), axis=0)
LsrNowArray = np.concatenate((np.zeros((1, TranShkGrid.size)), LsrNow), axis=0)
LsrNowArray[0, 0] = 1.0 # Don't work at all if TranShk=0, even if bNrm=0
LbrNowArray = 1.0 - LsrNowArray # Labor is the complement of leisure

Expand All @@ -284,14 +284,14 @@ def solveConsLaborIntMarg(
vPnvrsFuncNow_list = []
for j in range(TranShkGrid.size):
# Adjust bNrmNow for this transitory shock, so bNrmNow_temp[0] = 0
bNrmNow_temp = (bNowArray[:, j] - bNowArray[0, j])
bNrmNow_temp = (bNowArray[:, j] - bNowArray[0, j])

# Make consumption function for this transitory shock
cFuncNow_list.append(LinearInterp(bNrmNow_temp, cNowArray[:, j]))
cFuncNow_list.append(LinearInterp(bNrmNow_temp, cNowArray[:, j]))

# Make labor function for this transitory shock
LbrFuncNow_list.append(LinearInterp(bNrmNow_temp, LbrNowArray[:, j]))
LbrFuncNow_list.append(LinearInterp(bNrmNow_temp, LbrNowArray[:, j]))

# Make pseudo-inverse marginal value function for this transitory shock
vPnvrsFuncNow_list.append(LinearInterp(bNrmNow_temp, vPnvrsNowArray[:, j]))

Expand Down Expand Up @@ -324,7 +324,7 @@ class LaborIntMargConsumerType(IndShockConsumerType):
A class representing agents who make a decision each period about how much
to consume vs save and how much labor to supply (as a fraction of their time).
They get CRRA utility from a composite good x_t = c_t*z_t^alpha, and discount
future utility flows at a constant factor.
future utility flows at a constant factor.
"""

time_vary_ = copy(IndShockConsumerType.time_vary_)
Expand All @@ -336,12 +336,12 @@ def __init__(self, cycles=1, **kwds):
Instantiate a new consumer type with given data.
See init_labor_intensive for a dictionary of
the keywords that should be passed to the constructor.

Parameters
----------
cycles : int
Number of times the sequence of periods should be solved.

Returns
-------
None
Expand All @@ -357,11 +357,11 @@ def __init__(self, cycles=1, **kwds):
def update(self):
"""
Update the income process, the assets grid, and the terminal solution.

Parameters
----------
None

Returns
-------
None
Expand All @@ -376,11 +376,11 @@ def updateLbrCost(self):
Construct the age-varying cost of working LbrCost using the attribute LbrCostCoeffs.
This attribute should be a 1D array of arbitrary length, representing polynomial
coefficients (over t_cycle) for (log) LbrCost.

Parameters
----------
None

Returns
-------
None
Expand All @@ -403,13 +403,13 @@ def calcBoundingValues(self):
expected future income after receiving income this period, ignoring mort-
ality. The maximum MPC is the limit of the MPC as m --> mNrmMin. The
minimum MPC is the limit of the MPC as m --> infty.

NOT YET IMPLEMENTED FOR THIS CLASS

Parameters
----------
None

Returns
-------
None
Expand All @@ -423,9 +423,9 @@ def makeEulerErrorFunc(self, mMax=100, approx_inc_dstn=True):
Stores result in attribute eulerErrorFunc as an interpolated function.
Has option to use approximate income distribution stored in self.IncomeDstn
or to use a (temporary) very dense approximation.

NOT YET IMPLEMENTED FOR THIS CLASS

Parameters
----------
mMax : float
Expand All @@ -435,7 +435,7 @@ def makeEulerErrorFunc(self, mMax=100, approx_inc_dstn=True):
bution stored in self.IncomeDstn[0], or to use a very accurate
discrete approximation instead. When True, uses approximation in
IncomeDstn; when False, makes and uses a very dense approximation.

Returns
-------
None
Expand Down Expand Up @@ -520,11 +520,11 @@ def updateTranShkGrid(self):
which is created by the method updateIncomeProcess(). Simply takes the
transitory shock values and uses them as a state grid; can be improved.
Creates the attribute TranShkGrid.

Parameters
----------
None

Returns
-------
None
Expand All @@ -540,14 +540,14 @@ def updateTranShkGrid(self):
) # Run the method addToTimeVary from AgentType to add TranShkGrid as one parameter of time_vary list

def updateSolutionTerminal(self):
"""
"""
Updates the terminal period solution and solves for optimal consumption
and labor when there is no future.

Parameters
----------
None

Returns
-------
None
Expand Down Expand Up @@ -617,7 +617,7 @@ def updateSolutionTerminal(self):
def plotcFunc(self, t, bMin=None, bMax=None, ShkSet=None):
"""
Plot the consumption function by bank balances at a given set of transitory shocks.

Parameters
----------
t : int
Expand All @@ -631,7 +631,7 @@ def plotcFunc(self, t, bMin=None, bMax=None, ShkSet=None):
ShkSet : [float] or None
Array or list of transitory shocks at which to plot the consumption
function. If None, defaults to the TranShkGrid for this time period.

Returns
-------
None
Expand Down Expand Up @@ -660,7 +660,7 @@ def plotcFunc(self, t, bMin=None, bMax=None, ShkSet=None):
def plotLbrFunc(self, t, bMin=None, bMax=None, ShkSet=None):
"""
Plot the labor supply function by bank balances at a given set of transitory shocks.

Parameters
----------
t : int
Expand All @@ -674,7 +674,7 @@ def plotLbrFunc(self, t, bMin=None, bMax=None, ShkSet=None):
ShkSet : [float] or None
Array or list of transitory shocks at which to plot the labor supply
function. If None, defaults to the TranShkGrid for this time period.

Returns
-------
None
Expand Down
Loading