Stochastic Trends, Deterministic Trends and Business Cycle Turning Points
AbstractThis study examines the relationship between specifications for long-run output patterns and specifications for business cycle dynamics. In an application to US GDP, it is found that inferences about the nature of the trend in output are not robust to changes in the specification for short-run fluctuations. Similarly, the choice of which model best describes the transitory movements in output depends on the way in which the trend is specified. The empirical analysis makes use of Bayesian methods to compare non-nested models for US GDP. Inspection of the predictive likelihoods for the individual data points suggests that the information contained in the data is largely limited to the observations associated with business cycle turning points.
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Bibliographic InfoPaper provided by Université Laval - Département d'économique in its series Cahiers de recherche with number 9503.
Date of creation: 1995
Date of revision:
Other versions of this item:
- Gordon, Stephen, 1997. "Stochastic Trends, Deterministic Trends, and Business Cycle Turning Points," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(4), pages 411-34, July-Aug..
- NEP-ALL-1998-05-25 (All new papers)
- NEP-ECM-1998-05-25 (Econometrics)
- NEP-ETS-1998-05-25 (Econometric Time Series)
- NEP-TID-1998-05-25 (Technology & Industrial Dynamics)
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- Koskinen, Lasse & Öller, Lars-Erik, 2001.
"A Classifying Procedure for Signaling Turning Points,"
Working Paper Series in Economics and Finance
427, Stockholm School of Economics.
- Lars-Erik Öller & Lasse Koskinen, 2004. "A classifying procedure for signalling turning points," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(3), pages 197-214.
- Hans-Martin Krolzig & Michael Clements, 2000.
"Business Cycle Asymmetries: Characterisation and Testing based on Markov-Switching Autoregressions,"
Economics Series Working Papers
2000-W32, University of Oxford, Department of Economics.
- Clements, Michael P & Krolzig, Hans-Martin, 2003. "Business Cycle Asymmetries: Characterization and Testing Based on Markov-Switching Autoregressions," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(1), pages 196-211, January.
- Clements, M.P. & Krolzig, H-M., 1999. "Business Cycle Asymmetries: Characterisationand Testing Based on Markov-Switching Autoregression," The Warwick Economics Research Paper Series (TWERPS) 522, University of Warwick, Department of Economics.
- Chun Liu & John M Maheu, 2008.
"Forecasting Realized Volatility: A Bayesian Model Averaging Approach,"
tecipa-313, University of Toronto, Department of Economics.
- Chun Liu & John M. Maheu, 2009. "Forecasting realized volatility: a Bayesian model-averaging approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(5), pages 709-733.
- John M Maheu & Thomas H McCurdy, 2007. "Modeling foreign exchange rates with jumps," Working Papers tecipa-279, University of Toronto, Department of Economics.
- E. Andersson & D. Bock & M. Frisen, 2006. "Some statistical aspects of methods for detection of turning points in business cycles," Journal of Applied Statistics, Taylor & Francis Journals, vol. 33(3), pages 257-278.
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