Detecting Turning Points with Many Predictors through Hidden Markov Models
AbstractThis 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.
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Bibliographic InfoPaper provided by EconWPA in its series Econometrics with number 0407001.
Length: 34 pages
Date of creation: 04 Jul 2004
Date of revision:
Note: Type of Document - pdf; pages: 34. This paper is dedicated to an analysis of business cycle indicator leading to a stochastic recession index.
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Business Cycle; Markov Switching; Dynamic Factor; Coincident Indicators;
Find related papers by JEL classification:
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
- E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
This paper has been announced in the following NEP Reports:
- NEP-ALL-2004-07-11 (All new papers)
- NEP-ECM-2004-07-17 (Econometrics)
- NEP-ETS-2004-07-11 (Econometric Time Series)
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- 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.
- Peter Reinhard Hansen, 2001. "An Unbiased and Powerful Test for Superior Predictive Ability," Working Papers 2001-06, Brown University, Department of Economics.
- Marcelle Chauvet & Jeremy M. Piger, 2003.
"Identifying business cycle turning points in real time,"
Federal Reserve Bank of St. Louis, issue Mar, pages 47-61.
- Marcelle Chauvet & Jeremy Piger, 2002. "Identifying business cycle turning points in real time," Working Paper 2002-27, Federal Reserve Bank of Atlanta.
- Francis X. Diebold & Glenn D. Rudebusch, 1994.
"Measuring Business Cycles: A Modern Perspective,"
NBER Working Papers
4643, National Bureau of Economic Research, Inc.
- 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 969-96, November.
- 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 357-84, March.
- 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.
- Ferrara, Laurent, 2003. "A three-regime real-time indicator for the US economy," Economics Letters, Elsevier, vol. 81(3), pages 373-378, December.
- 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.
- Eva Andersson & David Bock & Marianne Frisén, 2004. "Detection of Turning Points in Business Cycles," Journal of Business Cycle Measurement and Analysis, OECD Publishing,CIRET, vol. 2004(1), pages 93-108.
- 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.
- Dean Croushore & Tom Stark, 1999.
"A real-time data set for macroeconomists,"
99-4, Federal Reserve Bank of Philadelphia.
- 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.
- Amine LAHIANI & Olivier SCAILLET, .
"Testing for threshold effect in ARFIMA models: Application to US unemployment rate data,"
Swiss Finance Institute Research Paper Series
08-42, Swiss Finance Institute.
- Lahiani, A. & Scaillet, O., 2009. "Testing for threshold effect in ARFIMA models: Application to US unemployment rate data," International Journal of Forecasting, Elsevier, vol. 25(2), pages 418-428.
- Benoit Bellone, 2005. "Classical Estimation of Multivariate Markov-Switching Models using MSVARlib," Econometrics 0508017, EconWPA.
- Benoit Bellone, 2004. "Une lecture probabiliste du cycle d’affaires américain," Econometrics 0407002, EconWPA, revised 28 Mar 2005.
- Mendoza, Liu & Morales, Daniel, 2012. "Constructing a real-time coincident recession index: an application to the Peruvian economy," Working Papers 2012-020, Banco Central de Reserva del Perú.
- Li, Yushu, 2012. "Wavelet Improvement in Turning Point Detection using a HMM Model," Working Papers 2012:14, Lund University, Department of Economics.
- Benoit Bellone, 2004. "MSVARlib: a new Gauss library to estimate multivariate Hidden Markov Models," Econometrics 0406004, EconWPA.
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