Identifying business cycle turning points with sequential Monte Carlo methods: an online and real-time application to the Euro area
We propose a new approach for detecting turning points and forecasting the level of economic activity in the business cycle. We make use of coincident indicators and of nonlinear and non-Gaussian latent variable models. We thus combine the ability of nonlinear models to capture the asymmetric features of the business cycle with information on the current state of the economy provided by coincident indicators. Our approach relies upon sequential Monte Carlo filtering techniques applied to time-nonhomogenous Markov-switching models. The transition probabilities are driven by a beta-distributed stochastic component and by a set of exogenous variables. We illustrate, in a full Bayesian and online context, the effectiveness of the methodology. We also measure its ability to identify turning points and to forecast the European business cycle on both realtime and last-revised data. Copyright © 2009 John Wiley & Sons, Ltd.
Volume (Year): 29 (2010)
Issue (Month): 1-2 ()
|Contact details of provider:|| Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966|
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
- 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.
- 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.
- Harding, Don & Pagan, Adrian, 2002.
"Dissecting the cycle: a methodological investigation,"
Journal of Monetary Economics,
Elsevier, vol. 49(2), pages 365-381, March.
- Don Harding & Adrian Pagan, 2000. "Disecting the Cycle: A Methodological Investigation," Econometric Society World Congress 2000 Contributed Papers 1164, Econometric Society.
- Chauvet, Marcelle & Piger, Jeremy, 2008.
"A Comparison of the Real-Time Performance of Business Cycle Dating Methods,"
Journal of Business & Economic Statistics,
American Statistical Association, vol. 26, pages 42-49, January.
- Marcelle Chauvet & Jeremy M. Piger, 2005. "A comparison of the real-time performance of business cycle dating methods," Working Papers 2005-021, Federal Reserve Bank of St. Louis.
- Potter, Simon M, 1995.
"A Nonlinear Approach to US GNP,"
Journal of Applied Econometrics,
John Wiley & Sons, Ltd., vol. 10(2), pages 109-125, April-Jun.
- 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.
- Francis X. Diebold & Robert S. Mariano, 1994.
"Comparing Predictive Accuracy,"
NBER Technical Working Papers
0169, National Bureau of Economic Research, Inc.
- Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
- Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-263, July.
- Jasra, Ajay & Doucet, Arnaud, 2008. "Stability of sequential Monte Carlo samplers via the Foster-Lyapunov condition," Statistics & Probability Letters, Elsevier, vol. 78(17), pages 3062-3069, December.
- Diebold & Rudebusch, "undated".
"Measuring Business Cycle: A Modern Perspective,"
_061, University of Pennsylvania.
- Massimiliano Caporin & Domenico Sartore, 2006. "Methodological aspects of time series back-calculation," Working Papers 2006_56, Department of Economics, University of Venice "Ca' Foscari".
- Francis X. Diebold & Glenn Rudebusch & Daniel Sichel, 1993.
"Further Evidence on Business-Cycle Duration Dependence,"
in: Business Cycles, Indicators and Forecasting, pages 255-284
National Bureau of Economic Research, Inc.
- Francis X. Diebold & Glenn D. Rudebusch & Daniel E. Sichel, 1991. "Further evidence on business cycle duration dependence," Working Papers 91-11, Federal Reserve Bank of Philadelphia.
- Chang-Jin Kim & Christian J. Murray, 2002. "Permanent and transitory components of recessions," Empirical Economics, Springer, vol. 27(2), pages 163-183.
- Sichel, Daniel E, 1991.
"Business Cycle Duration Dependence: A Parametric Approach,"
The Review of Economics and Statistics,
MIT Press, vol. 73(2), pages 254-260, 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.).
- 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.
- Kim, C-J., 1991.
"Dynamic Linear Models with Markov-Switching,"
91-8, York (Canada) - Department of Economics.
- 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.
- Durbin, James & Koopman, Siem Jan, 2012.
"Time Series Analysis by State Space Methods,"
Oxford University Press,
edition 2, number 9780199641178.
- Goldfeld, Stephen M. & Quandt, Richard E., 1973. "A Markov model for switching regressions," Journal of Econometrics, Elsevier, vol. 1(1), pages 3-15, March.
When requesting a correction, please mention this item's handle: RePEc:jof:jforec:v:29:y:2010:i:1-2:p:145-167. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Wiley-Blackwell Digital Licensing)or (Christopher F. Baum)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.