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Testing for Cointegration Rank Using Bayes Factors

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  • Sugita, Katsuhiro

Abstract

This paper proposes Bayesian methods for estimating the cointegration rank using Bayes factors. We consider natural conjugate priors for computing Bayes factors. First, we estimate the cointegrating vectors for each possible rank. Then, we compute the Bayes factors for each rank against 0 rank. Monte Carlo simulations show that using Bayes factor with conjugate priors produces fairly good results. We apply the method to demand for money in the US.

Suggested Citation

  • Sugita, Katsuhiro, 2002. "Testing for Cointegration Rank Using Bayes Factors," Economic Research Papers 269467, University of Warwick - Department of Economics.
    • Handle: RePEc:ags:uwarer:269467
    DOI: 10.22004/ag.econ.269467
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    References listed on IDEAS

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    1. Geweke, John, 1989. "Bayesian Inference in Econometric Models Using Monte Carlo Integration," Econometrica, Econometric Society, vol. 57(6), pages 1317-1339, November.
    2. Lucas, Robert E., 1988. "Money demand in the United States: A quantitative review," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 29(1), pages 137-167, January.
    3. Kleibergen, Frank & van Dijk, Herman K., 1994. "On the Shape of the Likelihood/Posterior in Cointegration Models," Econometric Theory, Cambridge University Press, vol. 10(3-4), pages 514-551, August.
    4. Goldfeld, Stephen M. & Sichel, Daniel E., 1990. "The demand for money," Handbook of Monetary Economics, in: B. M. Friedman & F. H. Hahn (ed.), Handbook of Monetary Economics, edition 1, volume 1, chapter 8, pages 299-356, Elsevier.
    5. Johansen, S., 2000. "A Small Sample Correction of the Test for Cointegrating Rank in the Vector Autoregressive Model," Economics Working Papers eco2000/15, European University Institute.
    6. Phillips, Peter C B & Ploberger, Werner, 1996. "An Asymptotic Theory of Bayesian Inference for Time Series," Econometrica, Econometric Society, vol. 64(2), pages 381-412, March.
    7. BAUWENS, Luc & GIOT, Pierre, 1997. "A Gibbs sampling approach to cointegration," LIDAM Discussion Papers CORE 1997016, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    8. Geweke, John, 1996. "Bayesian reduced rank regression in econometrics," Journal of Econometrics, Elsevier, vol. 75(1), pages 121-146, November.
    9. Evans, Michael & Swartz, Timothy, 2000. "Approximating Integrals via Monte Carlo and Deterministic Methods," OUP Catalogue, Oxford University Press, number 9780198502784.
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    Cited by:

    1. Karlsson, Sune, 2013. "Forecasting with Bayesian Vector Autoregression," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 791-897, Elsevier.
    2. Gareth W. Peters & Balakrishnan Kannan & Ben Lasscock & Chris Mellen, 2010. "Model Selection and Adaptive Markov chain Monte Carlo for Bayesian Cointegrated VAR model," Papers 1004.3830, arXiv.org.
    3. Gareth W. Peters & Balakrishnan B. Kannan & Ben Lasscock & Chris Mellen & Simon Godsill, 2010. "Bayesian Cointegrated Vector Autoregression models incorporating Alpha-stable noise for inter-day price movements via Approximate Bayesian Computation," Papers 1008.0149, arXiv.org.

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