Numerically stable and accurate stochastic simulation approaches for solving dynamic economic models

Kenneth Judd, Lilia Maliar and Serguei Maliar

Working Papers. Serie AD from Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie)

Abstract: We develop numerically stable and accurate stochastic simulation approaches for solving dynamic economic models. First, instead of standard least-squares methods, we examine a variety of alternatives, including least-squares methods using singular value decomposition and Tikhonov regularization, least-absolute deviations methods, and principal component regression method, all of which are numerically stable and can handle ill-conditioned problems. Second, instead of conventional Monte Carlo integration, we use accurate quadrature and monomial integration. We test our generalized stochastic simulation algorithm (GSSA) in three applications: the standard representative agent neoclassical growth model, a model with rare disasters and a multi-country models with hundreds of state variables. GSSA is simple to program, and MATLAB codes are provided.

Keywords: Stochastic simulation; generalized stochastic simulation algorithm (GSSA), parameterized expectations algorithm (PEA); least absolute deviations (LAD); linear programming; regularization. (search for similar items in EconPapers)
JEL-codes: C63 C68 (search for similar items in EconPapers)
Pages: 61 pages
Date: 2011-07
New Economics Papers: this item is included in nep-cmp, nep-dge and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (119)

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http://www.ivie.es/downloads/docs/wpasad/wpasad-2011-15.pdf Fisrt version / Primera version, 2011 (application/pdf)

Related works:
Journal Article: Numerically stable and accurate stochastic simulation approaches for solving dynamic economic models (2011)
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Persistent link: https://EconPapers.repec.org/RePEc:ivi:wpasad:2011-15

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