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.

class HARK.ConsumptionSaving.ConsPortfolioFrameModel.PortfolioConsumerFrameType(**kwds)

Bases: 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.


Birth value for aNrmNow


Birth value for pLvlNow

model = <HARK.frame.FrameModel object at 0x7f65ff3cc640. Parameters: cycles: 1 CRRA: 5.0 Rfree: 1.03 DiscFac: 0.9 LivPrb: [0.98] PermGroFac: [1.01] BoroCnstArt: 0.0 MaxKinks: 400 AgentCount: 10000 aNrmInitMean: 0.0 aNrmInitStd: 1.0 pLvlInitMean: 0.0 pLvlInitStd: 0.0 PermGroFacAgg: 1.0 T_age: None T_cycle: 1 PerfMITShk: False aXtraMin: 0.001 aXtraMax: 100 aXtraNestFac: 1 aXtraCount: 200 aXtraExtra: [None] PermShkStd: [0.1] PermShkCount: 7 TranShkStd: [0.1] TranShkCount: 7 UnempPrb: 0.05 UnempPrbRet: 0.005 IncUnemp: 0.3 IncUnempRet: 0.0 tax_rate: 0.0 T_retire: 0 vFuncBool: False CubicBool: False neutral_measure: False NewbornTransShk: False RiskyAvg: 1.08 RiskyStd: 0.2 RiskyCount: 5 ShareCount: 25 AdjustPrb: 1.0 DiscreteShareBool: False>

Solve the model for this instance of an agent type by backward induction. Loops through the sequence of one period problems, passing the solution from period t+1 to the problem for period t.


verbose (boolean) – If True, solution progress is printed to screen.

Return type: