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PortfolioFrameModel and FrameAgentType
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,294 @@ | ||
| """ | ||
| This file contains classes and functions for representing, | ||
| solving, and simulating agents who must allocate their resources | ||
| among consumption, saving in a risk-free asset (with a low return), | ||
| and saving in a risky asset (with higher average return). | ||
| This file also demonstrates a "frame" model architecture. | ||
| """ | ||
| import numpy as np | ||
| from scipy.optimize import minimize_scalar | ||
| from copy import deepcopy | ||
| from HARK import NullFunc, Frame, FrameAgentType # Basic HARK features | ||
| from HARK.ConsumptionSaving.ConsIndShockModel import ( | ||
| IndShockConsumerType, # PortfolioConsumerType inherits from it | ||
| utility, # CRRA utility function | ||
| utility_inv, # Inverse CRRA utility function | ||
| utilityP, # CRRA marginal utility function | ||
| utility_invP, # Derivative of inverse CRRA utility function | ||
| utilityP_inv, # Inverse CRRA marginal utility function | ||
| init_idiosyncratic_shocks, # Baseline dictionary to build on | ||
| ) | ||
| from HARK.ConsumptionSaving.ConsRiskyAssetModel import ( | ||
| RiskyAssetConsumerType | ||
| ) | ||
| from HARK.ConsumptionSaving.ConsPortfolioModel import ( | ||
| init_portfolio, | ||
| solveConsPortfolio, | ||
| PortfolioConsumerType, | ||
| PortfolioSolution | ||
| ) | ||
|
|
||
| from HARK.distribution import combine_indep_dstns, add_discrete_outcome_constant_mean | ||
| from HARK.distribution import ( | ||
| IndexDistribution, | ||
| Lognormal, | ||
| MeanOneLogNormal, | ||
| Bernoulli # Random draws for simulating agents | ||
| ) | ||
| from HARK.interpolation import ( | ||
|
|
||
| LinearInterp, # Piecewise linear interpolation | ||
| CubicInterp, # Piecewise cubic interpolation | ||
| LinearInterpOnInterp1D, # Interpolator over 1D interpolations | ||
| BilinearInterp, # 2D interpolator | ||
| ConstantFunction, # Interpolator-like class that returns constant value | ||
| IdentityFunction, # Interpolator-like class that returns one of its arguments | ||
| ValueFuncCRRA, | ||
| MargValueFuncCRRA, | ||
| MargMargValueFuncCRRA | ||
| ) | ||
|
|
||
| class PortfolioConsumerFrameType(FrameAgentType, PortfolioConsumerType): | ||
| """ | ||
| A consumer type with a portfolio choice, using Frame architecture. | ||
| A subclass of PortfolioConsumerType for now. | ||
| This is mainly to keep the _solver_ logic intact. | ||
| """ | ||
|
|
||
| def __init__(self, **kwds): | ||
| params = init_portfolio.copy() | ||
| params.update(kwds) | ||
| kwds = params | ||
|
|
||
| # Initialize a basic consumer type | ||
| PortfolioConsumerType.__init__( | ||
| self, **kwds | ||
| ) | ||
|
|
||
| # Set the solver for the portfolio model, and update various constructed attributes | ||
| self.solve_one_period = solveConsPortfolio | ||
| self.update() | ||
|
|
||
| ## TODO: Should be defined in the configuration. | ||
| self.aggs = {'PermShkAggNow' : None, 'PlvlAgg' : None, 'Risky' : None} # aggregate values | ||
| # -- handled differently because only one value each per AgentType | ||
| self.shocks = {'Adjust' : None, 'PermShk' : None, 'TranShk' : None} | ||
| self.controls = {'cNrm' : None, 'Share' : None} | ||
| self.state_now = { | ||
| 'Rport' : None, | ||
| 'aLvl' : None, | ||
| 'aNrm' : None, | ||
| 'bNrm' : None, | ||
| 'mNrm' : None, | ||
| 'pLvl' : None | ||
| } | ||
|
|
||
| # TODO: streamline this so it can draw the parameters from context | ||
| def birth_aNrmNow(self, N): | ||
| """ | ||
| Birth value for aNrmNow | ||
| """ | ||
| return Lognormal( | ||
| mu=self.aNrmInitMean, | ||
| sigma=self.aNrmInitStd, | ||
| seed=self.RNG.randint(0, 2 ** 31 - 1), | ||
| ).draw(N) | ||
|
|
||
| # TODO: streamline this so it can draw the parameters from context | ||
| def birth_pLvlNow(self, N): | ||
| """ | ||
| Birth value for pLvlNow | ||
| """ | ||
| pLvlInitMeanNow = self.pLvlInitMean + np.log( | ||
| self.aggs["PlvlAgg"] | ||
| ) # Account for newer cohorts having higher permanent income | ||
|
|
||
| return Lognormal( | ||
| pLvlInitMeanNow, | ||
| self.pLvlInitStd, | ||
| seed=self.RNG.randint(0, 2 ** 31 - 1) | ||
| ).draw(N) | ||
|
|
||
| def transition_Rport(self, **context): | ||
|
|
||
| Rport = ( | ||
| context["Share"] * context["Risky"] | ||
| + (1.0 - context["Share"]) * self.parameters['Rfree'] | ||
| ) | ||
| return Rport | ||
|
|
||
| def transition(self, **context): | ||
| pLvlPrev = context['pLvl'] | ||
| aNrmPrev = context['aNrm'] | ||
|
|
||
| # This should be computed separately in its own transition | ||
| # Using IndShock get_Rfree instead of generic. | ||
| RfreeNow = context['Rport'] | ||
|
|
||
| # Calculate new states: normalized market resources and permanent income level | ||
| pLvlNow = pLvlPrev * context['PermShk'] # Updated permanent income level | ||
|
|
||
| # Updated aggregate permanent productivity level | ||
| PlvlAggNow = context['PlvlAgg'] * context['PermShkAggNow'] | ||
| # "Effective" interest factor on normalized assets | ||
| ReffNow = RfreeNow / context['PermShk'] | ||
| bNrmNow = ReffNow * aNrmPrev # Bank balances before labor income | ||
| mNrmNow = bNrmNow + context['TranShk'] # Market resources after income | ||
|
|
||
| return pLvlNow, PlvlAggNow, bNrmNow, mNrmNow | ||
|
|
||
| def transition_ShareNow(self, **context): | ||
| """ | ||
| Transition method for ShareNow. | ||
| """ | ||
| ## Changed from HARK. See #1049. Should be added to context. | ||
| ShareNow = self.controls['Share'].copy() | ||
|
|
||
| # Loop over each period of the cycle, getting controls separately depending on "age" | ||
| for t in range(self.T_cycle): | ||
| these = t == self.t_cycle | ||
|
|
||
| # Get controls for agents who *can* adjust their portfolio share | ||
| those = np.logical_and(these, context['Adjust']) | ||
|
|
||
| ShareNow[those] = self.solution[t].ShareFuncAdj(context['mNrm'][those]) | ||
|
|
||
| # Get Controls for agents who *can't* adjust their portfolio share | ||
| those = np.logical_and( | ||
| these, | ||
| np.logical_not(context['Adjust'])) | ||
| ShareNow[those] = self.solution[t].ShareFuncFxd( | ||
| context['mNrm'][those], ShareNow[those] | ||
| ) | ||
|
|
||
| return ShareNow, | ||
|
|
||
| def transition_cNrmNow(self, **context): | ||
| """ | ||
| Transition method for cNrmNow. | ||
| """ | ||
| cNrmNow = np.zeros(self.AgentCount) + np.nan | ||
| ShareNow = context["Share"] | ||
|
|
||
| # Loop over each period of the cycle, getting controls separately depending on "age" | ||
| for t in range(self.T_cycle): | ||
| these = t == self.t_cycle | ||
|
|
||
| # Get controls for agents who *can* adjust their portfolio share | ||
| those = np.logical_and(these, context['Adjust']) | ||
| cNrmNow[those] = self.solution[t].cFuncAdj(context['mNrm'][those]) | ||
|
|
||
| # Get Controls for agents who *can't* adjust their portfolio share | ||
| those = np.logical_and( | ||
| these, | ||
| np.logical_not(context['Adjust'])) | ||
| cNrmNow[those] = self.solution[t].cFuncFxd( | ||
| context['mNrm'][those], ShareNow[those] | ||
| ) | ||
|
|
||
| return cNrmNow, | ||
|
|
||
| def transition_poststates(self, **context): | ||
| """ | ||
| Calculates end-of-period assets for each consumer of this type. | ||
| Parameters | ||
| ---------- | ||
| None | ||
| Returns | ||
| ------- | ||
| None | ||
| """ | ||
| # should this be "Now", or "Prev"?!? | ||
| # todo: don't store on self | ||
| self.state_now['aNrm'] = context['mNrm'] - context['cNrm'] | ||
| # Useful in some cases to precalculate asset level | ||
| self.state_now['aLvl'] = context['aNrm'] * context['pLvl'] | ||
|
|
||
| return (self.state_now['aNrm'], self.state_now['aLvl']) | ||
|
|
||
| # maybe replace reference to init_portfolio to self.parameters? | ||
| frames = [ | ||
| # todo : make an aggegrate value | ||
| Frame(('PermShkAggNow',), ('PermGroFacAgg',), | ||
| transition = lambda self, PermGroFacAgg : (PermGroFacAgg,) | ||
| ), | ||
| Frame( | ||
| ('PermShk'), None, | ||
| default = {'PermShk' : 1.0}, # maybe this is unnecessary because the shock gets sampled at t = 0 | ||
| # this is discretized before it's sampled | ||
| transition = IndexDistribution( | ||
| Lognormal.from_mean_std, | ||
| { | ||
| 'mean' : init_portfolio['PermGroFac'], | ||
| 'std' : init_portfolio['PermShkStd'] | ||
| } | ||
| ).approx( | ||
| init_portfolio['PermShkCount'], tail_N=0 | ||
| ), | ||
| ), | ||
| Frame( | ||
| ('TranShk'), None, | ||
| default = {'TranShk' : 1.0}, # maybe this is unnecessary because the shock gets sampled at t = 0 | ||
| transition = add_discrete_outcome_constant_mean( | ||
| IndexDistribution( | ||
| MeanOneLogNormal, | ||
| { | ||
| 'sigma' : init_portfolio['TranShkStd'] | ||
| }).approx( | ||
| init_portfolio['TranShkCount'], tail_N=0 | ||
| ), | ||
| p = init_portfolio['UnempPrb'], x = init_portfolio['IncUnemp'] | ||
| ) | ||
| ), | ||
| Frame( ## TODO: Handle Risky as an Aggregate value | ||
| ('Risky'),None, | ||
| transition = IndexDistribution( | ||
| Lognormal.from_mean_std, | ||
| { | ||
| 'mean' : init_portfolio['RiskyAvg'], | ||
| 'std' : init_portfolio['RiskyStd'] | ||
| } | ||
| # seed=self.RNG.randint(0, 2 ** 31 - 1) : TODO: Seed logic | ||
| ).approx( | ||
| init_portfolio['RiskyCount'] | ||
| ) | ||
| ), | ||
| Frame( | ||
| ('Adjust'),None, | ||
| default = {'Adjust' : False}, | ||
| transition = IndexDistribution( | ||
| Bernoulli, | ||
| {'p' : init_portfolio['AdjustPrb']}, | ||
| # seed=self.RNG.randint(0, 2 ** 31 - 1) : TODO: Seed logic | ||
| ) # self.t_cycle input implied | ||
| ), | ||
| Frame( | ||
| ('Rport'), ('Share', 'Risky'), | ||
| transition = transition_Rport | ||
| ), | ||
| ## TODO risk free return rate | ||
| Frame( | ||
| ('pLvl', 'PlvlAgg', 'bNrm', 'mNrm'), | ||
| ('pLvl', 'aNrm', 'Rport', 'PlvlAgg', 'PermShk', 'TranShk', 'PermShkAggNow'), | ||
| default = {'pLvl' : birth_pLvlNow, 'PlvlAgg' : 1.0}, | ||
| transition = transition | ||
| ), | ||
| Frame( | ||
| ('Share'), ('Adjust', 'mNrm'), | ||
| default = {'Share' : 0}, | ||
| transition = transition_ShareNow | ||
| ), | ||
| Frame( | ||
| ('cNrm'), ('Adjust','mNrm','Share'), | ||
| transition = transition_cNrmNow | ||
| ), | ||
| Frame( | ||
| ('aNrm', 'aLvl'), ('aNrm', 'cNrm', 'mNrm', 'pLvl'), | ||
| default = {'aNrm' : birth_aNrmNow}, | ||
| transition = transition_poststates | ||
| ) | ||
| ] |
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