Numerically Stable Stochastic Simulation Approaches for Solving Dynamic Economic Models

Kenneth L. Judd (), Lilia Maliar and Serguei Maliar

No 15296, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: We develop numerically stable stochastic simulation approaches for solving dynamic economic models. We rely on standard simulation procedures to simultaneously compute an ergodic distribution of state variables, its support and the associated decision rules. We differ from existing methods, however, in how we use simulation data to approximate decision rules. Instead of the usual least-squares approximation methods, we examine a variety of alternatives, including the least-squares method using SVD, Tikhonov regularization, least-absolute deviation methods, principal components regression method, all of which are numerically stable and can handle ill-conditioned problems. These new methods enable us to compute high-order polynomial approximations without encountering numerical problems. Our approaches are especially well suitable for high-dimensional applications in which other methods are infeasible.

JEL-codes: C63 C68 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cba, nep-cmp and nep-dge
Date: 2009-08
Note: EFG TWP
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