A narrative approach to a fiscal DSGE model
Quantitative Economics, 2020, vol. 11, issue 2, 801-837
Structural DSGE models are used for analyzing both policy and the sources of business cycles. Conclusions based on full structural models are, however, potentially affected by misspecification. A competing method is to use partially identified SVARs based on narrative shocks. This paper asks whether both approaches agree. Specifically, I use narrative data in a DSGE‐SVAR that partially identify policy shocks in the VAR and assess the fit of the DSGE model relative to this narrative benchmark. In developing this narrative DSGE‐SVAR, I develop a tractable Bayesian approach to proxy VARs and show that such an approach is valid for models with a certain class of Taylor rules. Estimating a DSGE‐SVAR based on a standard DSGE model with fiscal rules and narrative data, I find that the DSGE model identification is at odds with the narrative information as measured by the marginal likelihood. I trace this discrepancy to differences in impulse responses, identified historical shocks and policy rules. The results indicate monetary accommodation of fiscal shocks.
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Working Paper: A narrative approach to a fiscal DSGE model (2016)
Working Paper: A Narrative Approach to a Fiscal DSGE Model (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:wly:quante:v:11:y:2020:i:2:p:801-837
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