Lower Bounds on Approximation Errors to Numerical Solutions of Dynamic Economic Models

• 成果类型: Article
• 署名作者: Judd, Kenneth L.; Maliar, Lilia; Maliar, Serguei
• 署名单位: Stanford University; National Bureau of Economic Research; Stanford University; Universitat d'Alacant; Santa Clara University
• 刊物名称: ECONOMETRICA
• ISSN/ISSBN: 0012-9682
• DOI: 10.3982/ECTA12791
• 发表日期: 2017
• 页码: 991-1012
• 关键词: ACCURACY; simulation; perturbation; tests

摘要

We propose a novel methodology for evaluating the accuracy of numerical solutions to dynamic economic models. It consists in constructing a lower bound on the size of approximation errors. A small lower bound on errors is a necessary condition for accuracy: If a lower error bound is unacceptably large, then the actual approximation errors are even larger, and hence, the approximation is inaccurate. Our lower-bound error analysis is complementary to the conventional upper-error (worst-case) bound analysis, which provides a sufficient condition for accuracy. As an illustration of our methodology, we assess approximation in the first- and second-order perturbation solutions for two stylized models: a neoclassical growth model and a new Keynesian model. The errors are small for the former model but unacceptably large for the latter model under some empirically relevant parameterizations.