Open AccessFrequentist inference in weakly identified dynamic stochastic general equilibrium models
Author(s) -
GuerronQuintana Pablo,
Inoue Atsushi,
Kilian Lutz
Publication year - 2013
Publication title -
quantitative economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 4.062
H-Index - 27
eISSN - 1759-7331
pISSN - 1759-7323
DOI - 10.3982/qe306
Subject(s) - frequentist inference , estimator , bayes factor , inference , econometrics , mathematics , statistic , bayes' theorem , bayesian probability , point estimation , bayesian inference , posterior probability , test statistic , statistics , statistical hypothesis testing , computer science , artificial intelligence
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.