Comparing dynamic equilibrium models to data: a Bayesian approach

By Jesús Fernández-Villaverde, Juan F. Rubio-Ramirez. 2004

297 citations recorded by the source corpus.

Abstract This paper studies the properties of the Bayesian approach to estimation and comparison of dynamic equilibrium economies. Both tasks can be performed even if the models are nonnested, misspecified, and nonlinear. First, we show that Bayesian methods have a classical interpretation: asymptotically, the parameter point estimates converge to their pseudotrue values, and the best model under the Kullback–Leibler distance will have the highest posterior probability. Second, we illustrate the strong small sample behavior of the approach using a well-known application: the U.S. cattle cycle. Bayesian estimates outperform maximum likelihood results, and the proposed model is easily compared with a set of BVARs.

Subfields: Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/654bf5b7bd2519f3563c959c6af1b0d2e6b9d822

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In Economics and Econometrics

Citations in the corpus

Macroeconomics and Volatility: Data, Models, and Estimation (2010, background): “The advantages of the likelihood function as the center of inference have been explained in other places (see An and Schorfheide, 2006, Fernández-Villaverde and Rubio-Ramírez, 2004, and Fernández-Villaverde, 2010) and there is not much point in reviewing them here.”
Remittances and the Dutch disease (2009, unknown): “The Bayesian estimation technique uses a general equilibrium approach that addresses the identification problems of reduced-form models (see An and Schorfheide, 2007, for a survey), and outperforms GMM and maximum likelihood methods in small samples (Fernández-Villaverde and Rubio-Ramírez, 2004).”
Technology Shocks and Aggregate Fluctuations (2004, unknown): “We follow Fernández-Villaverde and Rubio-Ramírez (2004) and Lubik and Schorfheide (2003) and use the random walk Metropolis-Hastings algorithm, to obtain a random draw of size 500,000 from p ³ ψ| {xt}Tt=1 ,m ´ .”
Forecast Combination and Model Averaging Using Predictive Measures (2007, unknown): “We conjecture that the results of Fernández-Villaverde and Rubio-Ramírez (2004) apply to the partial Bayes factor as well, possibly with a rate condition on the limiting behavior of l/m.”
Bayesian Analysis of DSGE Models (2006, methodology): “It has been shown under various regularity conditions, e.g., Phillips (1996) and Fernández-Villaverde and Rubio-Ramírez (2004), that posterior odds (or their large sample approximations) asymptotically favor the DSGE model that is closest to the ‘true’ data generating process in the…”
Comparing New Keynesian Models of the Business Cycle: A Bayesian Approach (2005, unknown): “In addition, Fernández-Villaverde and Rubio-Ramírez (2004) show that, even in the case of misspeci ed models, Bayesian estimation and model comparison are consistent.”