Detecting Turning Points with Many Predictors through Hidden Markov Models
This paper explores the American business cycle with the Hidden Markov Model (HMM) as a monitoring tool using monthly data. It exhibits ten US time series which offer reliable information to detect recessions in real time. It also proposes and assesses the performances of different and complementary “recession models” based on Markovian processes, discusses the most efficient and easiest way of encompassing information through these models and draws three main conclusions: simple HMM are decisive to monitor the business cycle and some series are proved highly reliable; more sophisticated models such as the Dynamic Factor with Markov Switching (DFMS) model or Stock and Watson’s Experimental Recession Index seem not to be more powerful than simple (univariate or pseudo-multivariate) Hidden Markov Models, which remain far more parsimonious; combining information in temporal space seems to work marginally better than in probability space for high frequency data. We conclude about leading and “real time detection” properties related to HMM and give some hints for further research.
|Date of creation:||04 Jul 2004|
|Note:||Type of Document - pdf; pages: 34. This paper is dedicated to an analysis of business cycle indicator leading to a stochastic recession index.|
|Contact details of provider:|| Web page: http://econwpa.repec.org|
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- Croushore, Dean & Stark, Tom, 2001.
"A real-time data set for macroeconomists,"
Journal of Econometrics,
Elsevier, vol. 105(1), pages 111-130, November.
- Dean Croushore & Tom Stark, 1999. "A real-time data set for macroeconomists," Working Papers 99-4, Federal Reserve Bank of Philadelphia.
- Diebold, Francis X & Rudebusch, Glenn D, 1996. "Measuring Business Cycles: A Modern Perspective," The Review of Economics and Statistics, MIT Press, vol. 78(1), pages 67-77, February.
- Diebold & Rudebusch, "undated". "Measuring Business Cycle: A Modern Perspective," Home Pages _061, University of Pennsylvania.
- Francis X. Diebold & Glenn D. Rudebusch, 1994. "Measuring Business Cycles: A Modern Perspective," NBER Working Papers 4643, National Bureau of Economic Research, Inc.
- Harding, Don & Pagan, Adrian, 2003. "A comparison of two business cycle dating methods," Journal of Economic Dynamics and Control, Elsevier, vol. 27(9), pages 1681-1690, July.
- 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.
- 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.
- Chauvet, Marcelle, 1998. "An Econometric Characterization of Business Cycle Dynamics with Factor Structure and Regime Switching," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 969-996, November.
- Hamilton, James D & Perez-Quiros, Gabriel, 1996. "What Do the Leading Indicators Lead?," The Journal of Business, University of Chicago Press, vol. 69(1), pages 27-49, January.
- Marcelle Chauvet & Jeremy M. Piger, 2003. "Identifying business cycle turning points in real time," Review, Federal Reserve Bank of St. Louis, issue Mar, pages 47-61.
- Marcelle Chauvet & Jeremy M. Piger, 2002. "Identifying business cycle turning points in real time," FRB Atlanta Working Paper 2002-27, Federal Reserve Bank of Atlanta.
- Peter Reinhard Hansen, 2001. "An Unbiased and Powerful Test for Superior Predictive Ability," Working Papers 2001-06, Brown University, Department of Economics.
- Anas, Jacques & Ferrara, Laurent, 2002. "Un indicateur d'entrée et sortie de récession: application aux Etats-Unis
[A start-end recession index: Application for United-States]," MPRA Paper 4043, University Library of Munich, Germany.
- Eva Andersson & David Bock & Marianne Frisén, 2004. "Detection of Turning Points in Business Cycles," Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2004(1), pages 93-108.
- Stéphane Grégoir & Fabrice Lenglart, 1998. "Measuring the Probability of a Business Cycle Turning Point by Using a Multivariate Qualitative Hidden Markov Model," Working Papers 98-48, Centre de Recherche en Economie et Statistique.
- Gerhard Bry & Charlotte Boschan, 1971. "Cyclical Analysis of Time Series: Selected Procedures and Computer Programs," NBER Books, National Bureau of Economic Research, Inc, number bry_71-1, June.
- Christophe Croux & Mario Forni & Lucrezia Reichlin, 2001. "A Measure Of Comovement For Economic Variables: Theory And Empirics," The Review of Economics and Statistics, MIT Press, vol. 83(2), pages 232-241, May.
- Croux, Christophe & Forni, Mario & Reichlin, Lucrezia, 1999. "A Measure of Comovement for Economic Variables: Theory and Empirics," CEPR Discussion Papers 2339, C.E.P.R. Discussion Papers.
- Christophe Croux & Mario Forni & Lucrezia Reichlin, 2001. "A measure of co-movement for economic variables: theory and empirics," ULB Institutional Repository 2013/10139, ULB -- Universite Libre de Bruxelles.
- Ferrara, Laurent, 2003. "A three-regime real-time indicator for the US economy," Economics Letters, Elsevier, vol. 81(3), pages 373-378, December. Full references (including those not matched with items on IDEAS)