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A general endogenous grid method for multi-dimensional models with non-convexities and constraints

机译：具有非凸性和约束性的多维模型的通用内生网格方法

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摘要

The endogenous grid method (EGM) significantly speeds up the solution of stochastic dynamic programming problems by simplifying or completely eliminating root-finding. We propose a general and parsimonious EGM extended to handle (1) multiple continuous states and choices, (2) multiple occasionally binding constraints, and (3) non-convexities such as discrete choices. Our method enjoys the speed gains of the original one-dimensional EGM, while avoiding expensive interpolation on multi-dimensional irregular endogenous grids. We explicitly define a broad class of models for which our solution method is applicable, and illustrate its speed and accuracy using a consumption saving model with both liquid assets and illiquid pension assets and a discrete retirement choice. (C) 2016 Published by Elsevier B.V.
机译：内生网格方法（EGM）通过简化或完全消除根查找，大大加快了随机动态规划问题的解决速度。我们提出了一个通用的和简约的EGM，扩展来处理（1）多个连续状态和选择，（2）多个偶发性约束，以及（3）非凸性，例如离散选择。我们的方法享受了原始一维EGM的速度提升，同时避免了在多维不规则内生网格上进行昂贵的插值。我们明确定义了适用于我们的解决方案方法的广泛模型，并使用具有流动资产和非流动养老金资产的消费节省模型以及离散的退休选择来说明其速度和准确性。 （C）2016由Elsevier B.V.发布

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