A Composite Likelihood Framework for Analyzing Singular DSGE Models

Zhongjun Qu

No wp2015-002, Boston University - Department of Economics - Working Papers Series from Boston University - Department of Economics

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 the nonsingular submodels. Second, it provides an MCMC based procedure for parameter estimation. Third, it delivers confidence sets for the structural parameters and the impulse responses that allow for model misspecification. Fourth, it generates 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. Importantly, 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.

Keywords: business cycle; dynamic stochastic general equilibrium models; identification; impulse response; MCMC; stochastic singularity (search for similar items in EconPapers)
JEL-codes: C13 C32 C51 E1 (search for similar items in EconPapers)
Date: 2015-06
New Economics Papers: this item is included in nep-dge, nep-ecm, nep-ets, nep-mac and nep-sea
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Journal Article: A Composite Likelihood Framework for Analyzing Singular DSGE Models (2018)
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