Identifying Business Cycle Turning Points with Sequential Monte Carlo Methods
We apply sequential Monte Carlo (SMC) to the detection of turning points in the business cycle and to the evaluation of useful statistics employed in business cycle analysis. The proposed nonlinear filtering method is very useful for sequentially estimating the latent variables and the parameters of nonlinear and non-Gaussian time-series models, such as the Markov-switching (MS) models studied in this work. We show how to combine SMC with Monte Carlo Markov Chain for estimating time series models with MS latent factors. We illustrate the effectiveness of the methodology and measure, in a full Bayesian and realtime context, the ability of a pool of MS models to identify turning points in the European economic activity. We also compare our results with the business cycle datation existing in the literature and provide a sequential evaluation of the forecast accuracy of the competing MS models.
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- Chang-Jin Kim & Jeremy M. Piger, 2001.
"Common stochastic trends, common cycles, and asymmetry in economic fluctuations,"
2001-014, Federal Reserve Bank of St. Louis.
- Kim, Chang-Jin & Piger, Jeremy, 2002. "Common stochastic trends, common cycles, and asymmetry in economic fluctuations," Journal of Monetary Economics, Elsevier, vol. 49(6), pages 1189-1211, September.
- Chang-Jin Kim & Jeremy Piger, 2000. "Common Stochastic Trends, Common Cycles, and Asymmetry in Economic Fluctuations," Working Papers 0021, University of Washington, Department of Economics.
- Chang-Jin Kim & Jeremy Piger, 2000. "Common Stochastic Trends, Common Cycles, and Asymmetry in Economic Fluctuations," Discussion Papers in Economics at the University of Washington 0021, Department of Economics at the University of Washington.
- Chang-Jin Kim & Jeremy Piger, 2000. "Common Stochastic Trends, Common Cycles, and Asymmetry in Economic Fluctuations," Econometric Society World Congress 2000 Contributed Papers 1465, Econometric Society.
- Chang-Jin Kim & Jeremy M. Piger, 2000. "Common stochastic trends, common cycles, and asymmetry in economic fluctuations," International Finance Discussion Papers 681, Board of Governors of the Federal Reserve System (U.S.).
- Jacques Anas & Monica Billio & Laurent Ferrara & Gian Luigi Mazzi, 2008. "A System For Dating And Detecting Turning Points In The Euro Area," Manchester School, University of Manchester, vol. 76(5), pages 549-577, 09.
- Pierre Del Moral & Arnaud Doucet & Ajay Jasra, 2006. "Sequential Monte Carlo samplers," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(3), pages 411-436.
- Patrick Gagliardini & Christian Gourieroux, 2004.
"Stochastic Migration Models with Application to Corporate Risk,"
2004-35, Centre de Recherche en Economie et Statistique.
- Patrick Gagliardini, 2005. "Stochastic Migration Models with Application to Corporate Risk," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 3(2), pages 188-226.
- Don Harding & Adrian Pagan, 2000.
"Disecting the Cycle: A Methodological Investigation,"
Econometric Society World Congress 2000 Contributed Papers
1164, Econometric Society.
- Harding, Don & Pagan, Adrian, 2002. "Dissecting the cycle: a methodological investigation," Journal of Monetary Economics, Elsevier, vol. 49(2), pages 365-381, March.
- Diebold & Rudebusch, .
"Measuring Business Cycle: A Modern Perspective,"
_061, University of Pennsylvania.
- Bauwens, Luc & Lubrano, Michel & Richard, Jean-Francois, 2000. "Bayesian Inference in Dynamic Econometric Models," OUP Catalogue, Oxford University Press, number 9780198773139, December.
- 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.
- 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.
- 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.
- Jasra, Ajay & Doucet, Arnaud & Stephens, David A. & Holmes, Christopher C., 2008. "Interacting sequential Monte Carlo samplers for trans-dimensional simulation," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 1765-1791, January.
- Diebold, Francis X & Mariano, Roberto S, 2002.
"Comparing Predictive Accuracy,"
Journal of Business & Economic Statistics,
American Statistical Association, vol. 20(1), pages 134-44, January.
- Sichel, Daniel E, 1991.
"Business Cycle Duration Dependence: A Parametric Approach,"
The Review of Economics and Statistics,
MIT Press, vol. 73(2), pages 254-60, May.
- Daniel E. Sichel, 1989. "Business cycle duration dependence: a parametric approach," Working Paper Series / Economic Activity Section 98, Board of Governors of the Federal Reserve System (U.S.).
- Watson, Mark W, 1994.
"Business-Cycle Durations and Postwar Stabilization of the U.S. Economy,"
American Economic Review,
American Economic Association, vol. 84(1), pages 24-46, March.
- Mark W. Watson, 1992. "Business Cycle Durations and Postwar Stabilization of the U.S. Economy," NBER Working Papers 4005, National Bureau of Economic Research, Inc.
- Mark W. Watson, 1992. "Business cycle durations and postwar stabilization of the U.S. economy," Working Paper Series, Macroeconomic Issues 92-6, Federal Reserve Bank of Chicago.
- Jacques Anas & Laurent Ferrara, 2004. "Detecting Cyclical Turning Points: The ABCD Approach and Two Probabilistic Indicators," Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2004(2), pages 193-225.
- Kim, C-J., 1991.
"Dynamic Linear Models with Markov-Switching,"
91-8, York (Canada) - Department of Economics.
- Potter, Simon M, 1995.
"A Nonlinear Approach to US GNP,"
Journal of Applied Econometrics,
John Wiley & Sons, Ltd., vol. 10(2), pages 109-25, April-Jun.
- Durbin, James & Koopman, Siem Jan, 2001.
"Time Series Analysis by State Space Methods,"
Oxford University Press, number 9780198523543, December.
- Tom Doan, . "SEASONALDLM: RATS procedure to create the matrices for the seasonal component of a DLM," Statistical Software Components RTS00251, Boston College Department of Economics.
- Goldfeld, Stephen M. & Quandt, Richard E., 1973. "A Markov model for switching regressions," Journal of Econometrics, Elsevier, vol. 1(1), pages 3-15, March.
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