Estimating Dynamic Macroeconomic Models: How Informative are the Data?

32 Pages Posted: 3 Aug 2016

See all articles by Daniel O. Beltran

Daniel O. Beltran

Federal Reserve Board

David Draper

University of California, Santa Cruz

Date Written: 2016-08


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

JEL Classification: C11, C18, F41

Suggested Citation

Beltran, Daniel O. and Draper, David, Estimating Dynamic Macroeconomic Models: How Informative are the Data? (2016-08). FRB International Finance Discussion Paper No. 1175. Available at SSRN: or

Daniel O. Beltran (Contact Author)

Federal Reserve Board ( email )

20th Street and Constitution Avenue NW
Washington, DC 20551
United States

David Draper

University of California, Santa Cruz

1156 High St
Santa Cruz, CA 95064
United States

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