Approximate dynamic programming with post-decision states as a solution method for dynamic economic models |
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| Institution: | 1. Department of Chemistry, University of Montana, Missoula, MT 59812-1584, USA;2. Complexity Sciences Center, 9225 Collins Avenue, Suite 1208, Surfside, FL 33154, USA;3. Instituto de Altos Estudos da Paraíba, Rua Silvino Lopes 419–2502, 58039-190 João Pessoa, Brazil;4. Departamento de Física, Universidade Federal da Paraíba, 58051-970 João Pessoa, Brazil;5. Institute for Multiscale Simulation, Friedrich-Alexander Universität Erlangen-Nürnberg, D-91052 Erlangen, Germany;6. Department of Psychology, University of Montana, Missoula, MT 59812-1584, USA |
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| Abstract: | I introduce and evaluate a new stochastic simulation method for dynamic economic models. It is based on recent work in the operations research and engineering literatures (Van Roy et al., 1997, Powell, 2007, Bertsekas, 2011), but also had an early application in economics (Wright and Williams, 1982, Wright and Williams, 1984). The baseline method involves rewriting the household׳s dynamic program in terms of post-decision states. This makes it possible to choose controls optimally without computing an expectation. I add a subroutine to the original algorithm that updates the values of states not visited frequently on the simulation path; and adopt a stochastic stepsize that efficiently weights information. Finally, I modify the algorithm to exploit GPU computing. |
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| Keywords: | Numerical solutions Approximations Heterogeneous agents Nonlinear numerical solutions Dynamic programming |
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