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Bayesian inference and state number determination for hidden Markov models: an application to the information content of the yield curve about inflation

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  • Chopin, Nicolas
  • Pelgrin, Florian

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  • Chopin, Nicolas & Pelgrin, Florian, 2004. "Bayesian inference and state number determination for hidden Markov models: an application to the information content of the yield curve about inflation," Journal of Econometrics, Elsevier, vol. 123(2), pages 327-344, December.
    • Handle: RePEc:eee:econom:v:123:y:2004:i:2:p:327-344
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    References listed on IDEAS

    as
    1. Nicolas Chopin, 2001. "Sequential Inference and State Number Determination for Discrete State-Space Models through Particle Filtering," Working Papers 2001-34, Center for Research in Economics and Statistics.
    2. Albert, James H & Chib, Siddhartha, 1993. "Bayes Inference via Gibbs Sampling of Autoregressive Time Series Subject to Markov Mean and Variance Shifts," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(1), pages 1-15, January.
    3. Gerlach, Stefan, 1997. "The Information Content of the Term Structure: Evidence for Germany," Empirical Economics, Springer, vol. 22(2), pages 161-179.
    4. Nicolas Chopin, 2002. "A sequential particle filter method for static models," Biometrika, Biometrika Trust, vol. 89(3), pages 539-552, August.
    5. Jorion, Philippe & Mishkin, Frederic, 1991. "A multicountry comparison of term-structure forecasts at long horizons," Journal of Financial Economics, Elsevier, vol. 29(1), pages 59-80, March.
    6. 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.
    7. Hamilton, James D., 1996. "Specification testing in Markov-switching time-series models," Journal of Econometrics, Elsevier, vol. 70(1), pages 127-157, January.
    8. Walter R. Gilks & Carlo Berzuini, 2001. "Following a moving target—Monte Carlo inference for dynamic Bayesian models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(1), pages 127-146.
    9. L. Wasserman, 2000. "Asymptotic inference for mixture models by using data‐dependent priors," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(1), pages 159-180.
    10. Hamilton, James D., 1990. "Analysis of time series subject to changes in regime," Journal of Econometrics, Elsevier, vol. 45(1-2), pages 39-70.
    11. Frederic S. Mishkin, 1990. "The Information in the Longer Maturity Term Structure about Future Inflation," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 105(3), pages 815-828.
    12. Eric Jondeau & Roland Ricart, 1997. "Le contenu en information de la pente des taux : application au cas des titres publics fran ais," Working papers 43, Banque de France.
    13. Kozicki, Sharon & Tinsley, P.A., 2005. "What do you expect? Imperfect policy credibility and tests of the expectations hypothesis," Journal of Monetary Economics, Elsevier, vol. 52(2), pages 421-447, March.
    14. Fruhwirth-Schnatter S., 2001. "Markov Chain Monte Carlo Estimation of Classical and Dynamic Switching and Mixture Models," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 194-209, March.
    15. Kim, Chang-Jin & Nelson, Charles R. & Startz, Richard, 1998. "Testing for mean reversion in heteroskedastic data based on Gibbs-sampling-augmented randomization1," Journal of Empirical Finance, Elsevier, vol. 5(2), pages 131-154, June.
    16. Tzavalis, Elias & Wickens, M. R., 1996. "Forecasting inflation from the term structure," Journal of Empirical Finance, Elsevier, vol. 3(1), pages 103-122, May.
    17. Robert J. Shiller & John Y. Campbell & Kermit L. Schoenholtz, 1983. "Forward Rates and Future Policy: Interpreting the Term Structure of Interest Rates," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 14(1), pages 173-224.
    18. Goldfeld, Stephen M. & Quandt, Richard E., 1973. "A Markov model for switching regressions," Journal of Econometrics, Elsevier, vol. 1(1), pages 3-15, March.
    19. Éric Jondeau & Roland Ricart, 1999. "Le contenu en information de la pente des taux : application au cas des titres publics français," Économie et Prévision, Programme National Persée, vol. 140(4), pages 1-20.
    20. Sharon Kozicki, 1997. "Predicting real growth and inflation with the yield spread," Economic Review, Federal Reserve Bank of Kansas City, vol. 82(Q IV), pages 39-57.
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    Citations

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    Cited by:

    1. Gianni Amisano & Roberto Casarin, 2008. "Particle Filters for Markov-Switching Stochastic-Correlation Models," Working Papers 0814, University of Brescia, Department of Economics.
    2. Stéphane Adjemian & Florian Pelgrin, 2008. "Un regard bayésien sur les modèles dynamiques de la macroéconomie," Economie & Prévision, La Documentation Française, vol. 0(2), pages 127-152.
    3. repec:hal:spmain:info:hdl:2441/2129 is not listed on IDEAS
    4. repec:dau:papers:123456789/6830 is not listed on IDEAS
    5. Drew Creal, 2012. "A Survey of Sequential Monte Carlo Methods for Economics and Finance," Econometric Reviews, Taylor & Francis Journals, vol. 31(3), pages 245-296.
    6. He, Zhongfang & Maheu, John M., 2010. "Real time detection of structural breaks in GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2628-2640, November.
    7. Gary M. Koop & Simon M. Potter, 2004. "Forecasting and Estimating Multiple Change-point Models with an Unknown Number of Change-points," Discussion Papers in Economics 04/31, Division of Economics, School of Business, University of Leicester.
    8. Bellone, B. & Erwan Gautier & Le Coent, S., 2005. "Les march s financiers anticipent-ils les retournements conjoncturels?," Working papers 128, Banque de France.
    9. 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.
    10. Christopher Nam & John Aston & Adam Johansen, 2014. "Parallel sequential Monte Carlo samplers and estimation of the number of states in a Hidden Markov Model," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 66(3), pages 553-575, June.
    11. Wai‐Sum Chan & Li‐Xin Zhang & Siu Hung Cheung, 2009. "Temporal aggregation of Markov‐switching financial return models," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 25(3), pages 359-383, May.
    12. Tsasa Vangu, Jean-Paul Kimbambu, 2014. "Diagnostic de la politique monétaire en Rép. Dém. Congo – Approche par l’Equilibre Général Dynamique Stochastique," Dynare Working Papers 38, CEPREMAP.
    13. Matthieu Lemoine & Florian Pelgrin, 2003. "Introduction aux modèles espace-état et au filtre de Kalman," Revue de l'OFCE, Presses de Sciences-Po, vol. 86(3), pages 203-229.
    14. Didier Nibbering & Richard Paap & Michel van der Wel, 2016. "A Bayesian Infinite Hidden Markov Vector Autoregressive Model," Tinbergen Institute Discussion Papers 16-107/III, Tinbergen Institute, revised 13 Oct 2017.
    15. Hugh Christensen & Simon Godsill & Richard E Turner, 2020. "Hidden Markov Models Applied To Intraday Momentum Trading With Side Information," Papers 2006.08307, arXiv.org.
    16. repec:spo:wpmain:info:hdl:2441/2129 is not listed on IDEAS
    17. Nicolas Chopin, 2007. "Dynamic Detection of Change Points in Long Time Series," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 59(2), pages 349-366, June.
    18. Benoît Bellone & Erwan Gautier & Sébastien Le Coent, 2006. "Les marchés financiers anticipent-ils les retournements conjoncturels ?," Economie & Prévision, La Documentation Française, vol. 172(1), pages 83-99.
    19. Fabrice Murtin, 2007. "The Structural Change and the Endogeneity Bias of the College Premium in the United States 1968-2001"," Working Papers 2007-14, Center for Research in Economics and Statistics.

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