Can a Data-Rich Environment Help Identify the Sources of Model Misspecification?

36 Pages Posted: 29 Mar 2015

Date Written: March 27, 2015

Abstract

This paper proposes a method for detecting the sources of misspecification in a dynamic stochastic general equilibrium (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, and 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 simulation with a known data-generating process - and using a state-of-the-art model and US data up to 2011.

Keywords: DSGE Models, Model Misspecification, Bayesian Analysis

JEL Classification: C32, C52

Suggested Citation

Monti, Francesca, Can a Data-Rich Environment Help Identify the Sources of Model Misspecification? (March 27, 2015). Bank of England Working Paper No. 527, Available at SSRN: https://ssrn.com/abstract=2586064 or http://dx.doi.org/10.2139/ssrn.2586064

Francesca Monti (Contact Author)

Bank of England ( email )

Threadneedle Street
London, EC2R 8AH
United Kingdom

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