How much structure in empirical models?
Fabio Canova ()
Economics Working Papers from Department of Economics and Business, Universitat Pompeu Fabra
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; SVAR models; Identification; Invertibility; Misspecification; Small Samples. (search for similar items in EconPapers)
JEL-codes: C10 C52 E32 E50 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cba, nep-dge, nep-ecm and nep-mac
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Working Paper: How much structure in empirical models? (2008)
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Persistent link: https://EconPapers.repec.org/RePEc:upf:upfgen:1054
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