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Bayesian Inference for the Mixed-Frequency VAR Model

  • Paul Viefers
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    In this paper a mixed-frequency VAR à la Mariano & Murasawa (2004) with Markov regime switching in the parameters is estimated by Bayesian inference. Unlike earlier studies, that used the pseuo-EM algorithm of Dempster, Laird & Rubin (1977) to estimate the model, this paper describes how to make use of recent advances in Bayesian inference on mixture models. This way, one is able to surmount some well-known issues connected to inference on mixture models, e.g. the label switching problem. The paper features a numerical simulation study to gauge the model performance in terms of convergence to true parameter values and a small empirical example involving US business cycles.

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    File URL: http://www.diw.de/documents/publikationen/73/diw_01.c.388577.de/dp1172.pdf
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    Paper provided by DIW Berlin, German Institute for Economic Research in its series Discussion Papers of DIW Berlin with number 1172.

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    Length: 22 : Anh. p.
    Date of creation: 2011
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    Handle: RePEc:diw:diwwpp:dp1172
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    1. Eric Ghysels & Pedro Santa-Clara & Rossen Valkanov, 2004. "The MIDAS Touch: Mixed Data Sampling Regression Models," CIRANO Working Papers 2004s-20, CIRANO.
    2. Goldfeld, Stephen M. & Quandt, Richard E., 1973. "A Markov model for switching regressions," Journal of Econometrics, Elsevier, vol. 1(1), pages 3-15, March.
    3. Tommaso Proietti & Filippo Moauro, 2004. "Dynamic Factor Analysis with Nonlinear Temporal Aggregation Constraints," Econometrics 0401003, EconWPA.
    4. Koop, Gary & Korobilis, Dimitris, 2009. "Bayesian Multivariate Time Series Methods for Empirical Macroeconomics," MPRA Paper 20125, University Library of Munich, Germany.
    5. Kuzin, Vladimir & Marcellino, Massimiliano & Schumacher, Christian, 2011. "MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the euro area," International Journal of Forecasting, Elsevier, vol. 27(2), pages 529-542, April.
    6. John Geweke, 1991. "Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments," Staff Report 148, Federal Reserve Bank of Minneapolis.
    7. 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 949-68, November.
    8. Stephen Godfeld & Richard Quandt, 1973. "The Estimation Of Structural Shifts By Switching Regressions," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 2, number 4, pages 473-483 National Bureau of Economic Research, Inc.
    9. Hamilton, James D., 2011. "Calling recessions in real time," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1006-1026, October.
    10. Chang-Jin Kim & Charles R. Nelson, 1998. "Business Cycle Turning Points, A New Coincident Index, And Tests Of Duration Dependence Based On A Dynamic Factor Model With Regime Switching," The Review of Economics and Statistics, MIT Press, vol. 80(2), pages 188-201, May.
    11. Yasutomo Murasawa & Roberto S. Mariano, 2004. "Constructing a Coincident Index of Business Cycles Without Assuming a One-Factor Model," Econometric Society 2004 Far Eastern Meetings 710, Econometric Society.
    12. Zadrozny, Peter, 1988. "Gaussian Likelihood of Continuous-Time ARMAX Models When Data Are Stocks and Flows at Different Frequencies," Econometric Theory, Cambridge University Press, vol. 4(01), pages 108-124, April.
    13. Marin, Jean-Michel & Mengersen, Kerrie & Robert, Christian P., 2005. "Bayesian Modelling and Inference on Mixtures of Distributions," Economics Papers from University Paris Dauphine 123456789/6069, Paris Dauphine University.
    14. Christopher A. Sims, 1993. "A Nine-Variable Probabilistic Macroeconomic Forecasting Model," NBER Chapters, in: Business Cycles, Indicators and Forecasting, pages 179-212 National Bureau of Economic Research, Inc.
    15. John C. Robertson & Ellis W. Tallman, 1999. "Improving forecasts of the federal funds rate in a policy model," Working Paper 99-3, Federal Reserve Bank of Atlanta.
    16. Sylvia Fruhwirth-Schnatter, 2004. "Estimating marginal likelihoods for mixture and Markov switching models using bridge sampling techniques," Econometrics Journal, Royal Economic Society, vol. 7(1), pages 143-167, 06.
    17. Ivan Jeliazkov & Rui Liu, 2010. "A model-based ranking of U.S. recessions," Economics Bulletin, AccessEcon, vol. 30(3), pages 2289-2296.
    18. Kadiyala, K. Rao & Karlsson, Sune, 1994. "Numerical Aspects of Bayesian VAR-modeling," SSE/EFI Working Paper Series in Economics and Finance 12, Stockholm School of Economics.
    19. 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 357-84, March.
    20. Roberto S. Mariano & Yasutomo Murasawa, 2003. "A new coincident index of business cycles based on monthly and quarterly series," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(4), pages 427-443.
    21. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2004. "The MIDAS Touch: Mixed Data Sampling Regression Models," University of California at Los Angeles, Anderson Graduate School of Management qt9mf223rs, Anderson Graduate School of Management, UCLA.
    22. Konstantin A. KHOLODILIN, 2001. "Markov-Switching Common Dynamic Factor Model with Mixed-Frequency Data," Discussion Papers (IRES - Institut de Recherches Economiques et Sociales) 2001020, Université catholique de Louvain, Institut de Recherches Economiques et Sociales (IRES).
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