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Solving dynamic programming problems using stochastic grids and nearest-neighbor interpolation
Stockholm University, Faculty of Social Sciences, Department of Economics.
Stockholm University, Faculty of Social Sciences, Department of Economics.
(English)Manuscript (preprint) (Other academic)
Abstract [en]

We propose two modications to the method of endogenous grid points that greatly decreases the computational time for life cycle models with many exogenous state variables. First, we use simulated stochastic grids on the exogenous state variables. Second, when we interpolate to nd the continuation value of the model, we split the interpolation step into two: We use nearest-neighbor interpolation over the exogenous state variables, and multilinear interpolation over the endogenous state variables. We evaluate the numerical accuracy and computational eciency of the algorithm by solving a standard consumption/savings life-cycle model with an arbitrary number of exogenous state variables. The model with eight exogenous state variables is solved in around eight minutes on a standard desktop computer. We then demonstrate the usefulness of the algorithm by solving a model which includes a more realistic income process estimated by Guvenen et al. (2015).

National Category
Economics
Research subject
Economics
Identifiers
URN: urn:nbn:se:su:diva-155078OAI: oai:DiVA.org:su-155078DiVA, id: diva2:1196758
Available from: 2018-04-11 Created: 2018-04-11 Last updated: 2018-04-11Bibliographically approved
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