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Estimating Dynamic Macroeconomic Models: How Informative Are the Data?

Daniel Beltran () and David Draper

No 1175, International Finance Discussion Papers from Board of Governors of the Federal Reserve System (U.S.)

Abstract: Central banks have long used dynamic stochastic general equilibrium (DSGE) models, which are typically estimated using Bayesian techniques, to inform key policy decisions. This paper offers an empirical strategy that quantifies the information content of the data relative to that of the prior distribution. Using an off-the-shelf DSGE model applied to quarterly Euro Area data from 1970:3 to 2009:4, we show how Monte Carlo simulations can reveal parameters for which the model's structure obscures identification. By integrating out components of the likelihood function and conducting a Bayesian sensitivity analysis, we uncover parameters that are weakly informed by the data. The weak identification of some key structural parameters in our comparatively simple model should raise a red flag to researchers trying to draw valid inferences from, and to base policy upon, complex large-scale models featuring many parameters.

Keywords: Bayesian estimation; Econometric modeling; Kalman filter; Likelihood; Local identifcation; Euro Area; MCMC; Policy-relevant parameters; Prior-versus-posterior comparison; Sensitivity analysis (search for similar items in EconPapers)
JEL-codes: C11 C18 F41 (search for similar items in EconPapers)
Pages: 30 pages
Date: 2016-08
New Economics Papers: this item is included in nep-cmp, nep-dge, nep-ecm and nep-ore
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Journal Article: Estimating dynamic macroeconomic models: how informative are the data? (2018) Downloads
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Persistent link: https://EconPapers.repec.org/RePEc:fip:fedgif:1175

DOI: 10.17016/IFDP.2016.1175

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