How Much Structure in Empirical Models?

39 Pages Posted: 12 Jun 2008

See all articles by Fabio Canova

Fabio Canova

BI Norwegian Business School

Multiple version iconThere are 2 versions of this paper

Date Written: April 2008

Abstract

This chapter highlights the problems that structural methods and SVAR approaches have when estimating DSGE models and examining their ability to capture important features of the data. We show that structural methods are subject to severe identification problems due, in large part, to the nature of DSGE models. The problems can be patched up in a number of ways, but solved only if DSGEs are completely reparametrized or respecified. The potential misspecification of the structural relationships give Bayesian methods an hedge over classical ones in structural estimation. SVAR approaches may face invertibility problems but simple diagnostics can help to detect and remedy these problems. A pragmatic empirical approach ought to use the flexibility of SVARs against potential misspecification of the structural relationships but must firmly tie SVARs to the class of DSGE models which could have have generated the data.

Keywords: DSGE models, Identification, Invertibility, SVAR models

JEL Classification: C10, C52, E32, E50

Suggested Citation

Canova, Fabio, How Much Structure in Empirical Models? (April 2008). CEPR Discussion Paper No. DP6791, Available at SSRN: https://ssrn.com/abstract=1142173

Fabio Canova (Contact Author)

BI Norwegian Business School ( email )

Nydalsveien 37
Oslo, 0442
Norway

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