Working Paper: NBER ID: w16304
Authors: Serguei Maliar; Lilia Maliar; Kenneth L. Judd
Abstract: We use the stochastic simulation algorithm, described in Judd, Maliar and Maliar (2009), and the cluster-grid algorithm, developed in Judd, Maliar and Maliar (2010a), to solve a collection of multi-country real business cycle models. The following ingredients help us reduce the cost in high-dimensional problems: an endogenous grid enclosing the ergodic set, linear approximation methods, fixed-point iteration and efficient integration methods, such as non-product monomial rules and Monte Carlo integration combined with regression. We show that high accuracy in intratemporal choice is crucial for the overall accuracy of solutions and offer two approaches, precomputation and iteration-on-allocation, that can solve for intratemporal choice both accurately and quickly. We also implement a hybrid solution algorithm that combines the perturbation and accurate intratemporal-choice methods.
Keywords: Real Business Cycle; Multicountry Models; Ergodic Set Methods
JEL Codes: C63; C68
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| endogenous grid encloses ergodic set (D50) | cost reductions in high-dimensional problems (C55) |
| high accuracy in intratemporal choice (D87) | overall accuracy of solutions (C52) |
| iteration on allocation and precomputation (C69) | accurate and quick intratemporal choice (D87) |
| perturbation methods + accurate intratemporal choice (D15) | overall accuracy of solutions (C52) |
| CGA method (C68) | results of similar accuracy to costly methods (C52) |