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MCMC method for Markov mixture simultaneous-equation models: a note

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  • Christopher A. Sims
  • Tao Zha

Abstract

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.

Suggested Citation

  • Christopher A. Sims & Tao Zha, 2004. "MCMC method for Markov mixture simultaneous-equation models: a note," FRB Atlanta Working Paper 2004-15, Federal Reserve Bank of Atlanta.
    • Handle: RePEc:fip:fedawp:2004-15
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    File URL: https://www.frbatlanta.org/-/media/documents/research/publications/wp/2004/wp0415.pdf
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    References listed on IDEAS

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    1. Sims, Christopher A & Zha, Tao, 1998. "Bayesian Methods for Dynamic Multivariate Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 949-968, November.
    2. John Geweke, 1999. "Using simulation methods for bayesian econometric models: inference, development,and communication," Econometric Reviews, Taylor & Francis Journals, vol. 18(1), pages 1-73.
    3. John Geweke, 1999. "Using Simulation Methods for Bayesian Econometric Models," Computing in Economics and Finance 1999 832, Society for Computational Economics.
    4. Rudebusch, Glenn D. & Svensson, Lars E. O., 2002. "Eurosystem monetary targeting: Lessons from U.S. data," European Economic Review, Elsevier, vol. 46(3), pages 417-442, March.
    5. Waggoner, Daniel F. & Zha, Tao, 2003. "A Gibbs sampler for structural vector autoregressions," Journal of Economic Dynamics and Control, Elsevier, vol. 28(2), pages 349-366, November.
    6. James D. Hamilton & Daniel F. Waggoner & Tao Zha, 2007. "Normalization in Econometrics," Econometric Reviews, Taylor & Francis Journals, vol. 26(2-4), pages 221-252.
    7. Chang-Jin Kim & Charles R. Nelson, 1999. "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262112388, December.
    8. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    9. Taylor, John B., 1999. "The robustness and efficiency of monetary policy rules as guidelines for interest rate setting by the European central bank," Journal of Monetary Economics, Elsevier, vol. 43(3), pages 655-679, June.
    10. Chib S. & Jeliazkov I., 2001. "Marginal Likelihood From the Metropolis-Hastings Output," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 270-281, March.
    11. John B. Taylor, 1999. "Monetary Policy Rules," NBER Books, National Bureau of Economic Research, Inc, number tayl99-1, March.
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    Cited by:

    1. Mehmet Balcilar & Rangan Gupta & Kevin Kotzé, 2017. "Forecasting South African macroeconomic variables with a Markov-switching small open-economy dynamic stochastic general equilibrium model," Empirical Economics, Springer, vol. 53(1), pages 117-135, August.
    2. Sims, Christopher A. & Waggoner, Daniel F. & Zha, Tao, 2008. "Methods for inference in large multiple-equation Markov-switching models," Journal of Econometrics, Elsevier, vol. 146(2), pages 255-274, October.
    3. Christopher A. Sims & Tao Zha, 2006. "Were There Regime Switches in U.S. Monetary Policy?," American Economic Review, American Economic Association, vol. 96(1), pages 54-81, March.
    4. Kizys, Renatas & Paltalidis, Nikos & Vergos, Konstantinos, 2016. "The quest for banking stability in the euro area: The role of government interventions," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 40(C), pages 111-133.

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