MCMC Method for Markov Mixture Simultaneous-Equation Models: A Note
Christopher A. Sims and Tao Zha
Working Paper 2004-15
This paper extends the methods developed by Hamilton (1989) and Chib (1996) to identified multiple-equation models. It details how to obtain Bayesian estimation and inference for a class of models with different degrees of time variation and discusses both analytical and computational difficulties.
JEL classification: C3
Key words: simultaneity, identification, time variation, volatility, Bayesian method
The authors thank Dan Waggoner for helpful discussions. The views expressed here are the authors’ and not necessarily those of the Federal Reserve Bank of Atlanta or the Federal Reserve System. Any remaining errors are the authors’ responsibility.
Please address questions regarding content to Christopher A. Sims, Department of Economics, 104 Fisher Hall, Princeton University, Princeton, New Jersey 08544-1021, 609-258-4033, firstname.lastname@example.org, or Tao Zha, Research Department, Federal Reserve Bank of Atlanta, 1000 Peachtree Street N.E., Atlanta, Georgia 30309-4470, 404-498-8353, email@example.com.