Frequentist inference in weakly identified dynamic stochastic general equilibrium models: Acronyms must be spelled out in titles for indexing purposes

• 成果类型: Article
• 署名作者: Guerron-Quintana, Pablo; Inoue, Atsushi; Kilian, Lutz
• 署名单位: Federal Reserve System - USA; Federal Reserve Bank - Philadelphia; North Carolina State University; University of Michigan System; University of Michigan
• 刊物名称: QUANTITATIVE ECONOMICS
• ISSN/ISSBN: 1759-7323
• DOI: 10.3982/QE306
• 发表日期: 2013
• 页码: 197-229
• 关键词: DSGE models; identification; inference; confidence sets; Bayes factor; likelihood ratio; C32; C52; E30; E50

摘要

A common problem in estimating dynamic stochastic general equilibrium models is that the structural parameters of economic interest are only weakly identified. As a result, classical confidence sets and Bayesian credible sets will not coincide even asymptotically, and the mean, mode, or median of the posterior distribution of the structural parameters can no longer be viewed as a consistent estimator. We propose two methods of constructing confidence intervals for structural model parameters that are asymptotically valid from a frequentist point of view regardless of the strength of identification. One involves inverting a likelihood ratio test statistic, whereas the other involves inverting a Bayes factor statistic. A simulation study shows that both methods have more accurate coverage than alternative methods of inference. An empirical study of the degree of wage and price rigidities in the U.S. economy illustrates that the data may contain useful information about structural model parameters even when these parameters are only weakly identified.