The Quantitative Importance of News Shocks in Estimated DSGE Models

by Hashmat Khan and John Tsoukalas

We estimate a dynamic stochastic general equilibrium (DSGE) model with several frictions and shocks, including news shocks to total factor productivity (TFP) and investment-specic (IS) technology, using quarterly US data from 1954-2004 and Bayesian methods. When all types of shocks are considered, TFP news and IS news compete with other atemporal and intertemporal shocks and account for less than 1.5% and 0.15% of the unconditional variance of output growth, respectively. In the fleexible price-wage environment, the contributions of the two shocks are 2.4% and 0%, respectively. When we exclude an atemporal (price markup) shock, the role for TFP news rises but the t of that model is substantially poorer relative to the benchmark model. Based on the variance decompositions and impulse responses, our findings suggest that news shocks are likely to be less important in estimated sticky price-wage DSGE models relative to perfectly competitive models.

News shocks have recently become a popular avenue to study business cycles, following Beaudry and Portier (2004, 2006). But this paper seems to indicate that they are quantitatively of no importance. Should we still consider them?

2 Responses to The Quantitative Importance of News Shocks in Estimated DSGE Models

  1. pushmedia1 says:

    How do you answer that question? Is there a channel where news shocks can get some leverage that isn’t modeled in that paper?

  2. Hashmat Khan says:

    First of all, I’d like to thank Christian Zimmermann for starting this very useful and interesting DGE blog, and for choosing our paper for this week.

    Regarding your question: yes, to consider alternative mechanisms in DSGE models where news shocks might turn out to quantitatively important is clearly an open area and well worth investigating. In fact, in flexible price-wage environment with few atemporal shocks, that is the case, as we discuss in the paper. But that model does not have a good fit with the data relative to the benchmark model. The motivation behind the choice of the DSGE model we consider is provided on page 2 (second para) and Section 2, page 4 (first para).

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