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Federal Reserve Bank of Dallas
Globalization Institute Working Papers
Bayesian estimation of NOEM models: identification and inference in small samples
Enrique Martínez-García
Diego Vilán
Mark A. Wynne
Abstract

This paper studies the (potential) weak identification of these relationships in the context of a fully specified structural model using Bayesian estimation techniques. We trace the problems to sample size, rather than misspecification bias. We conclude that standard macroeconomic time series with a coverage of less than forty years are subject to potentially serious identification issues, and also to model selection errors. We recommend estimation with simulated data prior to bringing the model to the actual data as a way of detecting parameters that are susceptible to weak identification in short samples.


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Enrique Martínez-García & Diego Vilán & Mark A. Wynne, Bayesian estimation of NOEM models: identification and inference in small samples, Federal Reserve Bank of Dallas, Globalization Institute Working Papers 105, 2012, revised 01 Mar 2012.
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Note: Published as: Martínez-García, Enrique, Diego Vilán and Mark A. Wynne (2012), "Bayesian Estimation of NOEM Models: Identification and Inference in Small Samples," in DSGE Models in Macroeconomics: Estimation, Evaluation, and New Development, ed. Nathan Balke, Fabio Canova, Fabio Milani and Mark A. Wynne (Bingley, UK: Emerald Group Publishing Limited), 137-199.
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