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@sbenthall sbenthall released this 14 Dec 16:54
· 1652 commits to master since this release
0.12.0
a2b6917
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Learn about vigilant mode.

Major Changes

  • FrameAgentType for modular definitions of agents #865 #1064
  • Frame relationships with backward and forward references, with plotting example #1071
  • PortfolioConsumerFrameType, a port of PortfolioConsumerType to use Frames #865
  • Input parameters for cyclical models now indexed by t #1039
  • A IndexDistribution class for representing time-indexed probability distributions #1018.
  • Adds new consumption-savings-portfolio model RiskyContrib, which represents an agent who can save in risky and risk-free assets but faces
    frictions to moving funds between them. To circumvent these frictions, he has access to an income-deduction scheme to accumulate risky assets.
    PR: #832. See this forthcoming REMARK for the model's details.
  • 'cycles' agent property moved from constructor argument to parameter #1031
  • Uses iterated expectations to speed-up the solution of RiskyContrib when income and returns are independent #1058.
  • ConsPortfolioSolver class for solving portfolio choice model replaces solveConsPortfolio method #1047
  • ConsPortfolioDiscreteSolver class for solving portfolio choice model when allowed share is on a discrete grid #1047
  • ConsPortfolioJointDistSolver class for solving portfolio chioce model when the income and risky return shocks are not independent #1047

Minor Changes

  • Using Lognormal.from_mean_std in the forward simulation of the RiskyAsset model #1019
  • Fix bug in DCEGM's primary kink finder due to numpy no longer accepting NaN in integer arrays #990.
  • Add a general class for consumers who can save using a risky asset #1012.
  • Add Boolean attribute 'PerfMITShk' to consumption models. When true, allows perfect foresight MIT shocks to be simulated. #1013.
  • Track and update start-of-period (pre-income) risky and risk-free assets as states in the RiskyContrib model 1046.
  • distribute_params now uses assign_params to create consistent output #1044
  • The function that computes end-of-period derivatives of the value function was moved to the inside of ConsRiskyContrib's solver #1057
  • Use np.fill(np.nan) to clear or initialize the arrays that store simulations. #1068
  • Add Boolean attribute 'neutral_measure' to consumption models. When true, simulations are more precise by allowing permanent shocks to be drawn from a neutral measure (see Harmenberg 2021). #1069
  • Fix mathematical limits of model example in example_ConsPortfolioModel.ipynb #1047
  • Update ConsGenIncProcessModel.py to use calc_expectation method #1072
  • Fix bug in calc_normal_style_pars_from_lognormal_pars due to math error. #1076
  • Fix bug in distribute_params so that AgentCount parameter is updated. #1089
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