We thank a co-editor and two anonymous referees for very useful comments that led to a substantial improvement of the paper. Lilia Maliar and Serguei Maliar acknowledge support from the Hoover Institution at Stanford University, the Ivie, the Generalitat Valenciana under Grants BEST/2010/142 and BEST/2010/141, respectively, the Ministerio de Ciencia e Innovación de España, and FEDER funds under project SEJ-2007-62656 and under the programs José Castillejo JC2008-224 and Salvador Madariaga PR2008-190, respectively.
Numerically stable and accurate stochastic simulation approaches for solving dynamic economic models
Article first published online: 19 JUL 2011
Copyright © 2011 Kenneth L. Judd, Lilia Maliar, and Serguei Maliar
Volume 2, Issue 2, pages 173–210, July 2011
How to Cite
Judd, K. L., Maliar, L. and Maliar, S. (2011), Numerically stable and accurate stochastic simulation approaches for solving dynamic economic models. Quantitative Economics, 2: 173–210. doi: 10.3982/QE14
- Issue published online: 19 JUL 2011
- Article first published online: 19 JUL 2011
- Submitted August, 2009. Final version accepted January, 2011.
- Stochastic simulation;
- generalized stochastic simulation algorithm;
- parameterized expectations algorithm;
- least absolute deviations;
- linear programming;
We develop numerically stable and accurate stochastic simulation approaches for solving dynamic economic models. First, instead of standard least-squares approximation 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 multicountry model with hundreds of state variables. GSSA is simple to program, and MATLAB codes are provided.