A composite likelihood framework for analyzing singular DSGE models
Files
Accepted manuscript
Date
2018-12-02
DOI
Authors
Qu, Zhongjun
Version
Accepted manuscript
OA Version
Citation
Zhongjun Qu. 2018. "A composite likelihood framework for analyzing singular dsge models." The Review of Economics and Statistics, Volume 100, Issue 5, pp. 916 - 932 (17).
Abstract
This paper builds upon the composite likelihood concept of Lindsay (1988) to develop a framework for parameter identification, estimation, inference, and forecasting
in DSGE models allowing for stochastic singularity. The framework consists of the
following four components. First, it provides a necessary and sufficient condition for
parameter identification, where the identifying information is provided by the first and
second order properties of nonsingular submodels. Second, it provides an MCMC based
procedure for parameter estimation. Third, it delivers confidence sets for structural
parameters and impulse responses that allow for model misspecification. Fourth, it gen-
erates forecasts for all the observed endogenous variables, irrespective of the number of
shocks in the model. The framework encompasses the conventional likelihood analysis as a special case when the model is nonsingular. It enables the researcher to start with a basic model and then gradually incorporate more shocks and other features, meanwhile confronting all the models with the data to assess their implications. The methodology is illustrated using both small and medium scale DSGE models. These
models have numbers of shocks ranging between one and seven.