Can a data-rich environment help identify the sources of model misspecification?

Francesca Monti

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: This paper proposes a method for detecting the sources of misspecification in a DSGE model based on testing, in a data-rich environment, the exogeneity of the variables of the DSGE with respect to some auxiliary variables. Finding evidence of non-exogeneity implies misspecification, but finding that some specific variables help predict certain shocks can shed light on the dimensions along which the model is misspecified. Forecast error variance decomposition analysis then helps assess the relevance of the missing channels. The paper puts the proposed methodology to work both in a controlled experiment - by running a Monte Carlo simulations with a known DGP - and using a state-of-the-art model and US data up to 2011.

Keywords: DSGE Models; Model Misspecification; Bayesian Analysis (search for similar items in EconPapers)
JEL-codes: C32 C52 (search for similar items in EconPapers)
Pages: 35 pages
Date: 2015-01-30
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Downloads: (external link)
http://eprints.lse.ac.uk/86320/ Open access version. (application/pdf)

Related works:
Working Paper: Can a data-rich environment help identify the sources of model misspecification? (2015)
Working Paper: Can a data-rich environment help identify the sources of model misspecification? (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:86320

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