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

Working papers set out research in progress by our staff, with the aim of encouraging comments and debate.
Published on 27 March 2015

Working Paper No. 527
By Francesca Monti 

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.

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