Cogley and Sargent provide us with a very useful tool for empirical macroeconomics: a Gibbs sampler for the estimation of VARs with drifting coefficients and volatilities. The authors apply the tool to a VAR with three variables—inflation, unemployment, and the nominal interest rate—and two lags. This tool is a serious competitor to the identified-VAR-cum-Markov-switching technology recently developed by Sims (1999) and Sims and Zha (2002) for the study of economies that are subject to regime changes. However, the Gibbs sampler suffers from a curse of dimensionality: as more variables or more lags are added to the system, the computational burden of the estimation quickly grows out of proportion. My suggestions here are mainly aimed at making the tool more flexible, and hence more widely applicable.
I wish to thank Tim Cogley for providing the matlab programs and the data used in the paper, and Dan Waggoner and Tao Zha for helpful conversations. The discussant’s comments were presented at the Monetary Policy and Learning Conference sponsored by the Federal Reserve Bank of Atlanta in March 2003. The views expressed here are the author’s and not necessarily those of the Federal Reserve Bank of Atlanta or the Federal Reserve System. Any remaining errors are the author’s responsibility.
Please address questions regarding content to Marco Del Negro, Research Department, Federal Reserve Bank of Atlanta, 1000 Peachtree Street, N.E., Atlanta, Georgia 30309-4470, 404-498-8561, 404-498-8956 (fax), Marco.DelNegro@atl.frb.org.
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