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Learning, Forecasting and Structural Breaks Author info | Abstract | Publisher info | Download info | Related research | Statistics John M. Maheu
Stephen Gordon
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The literature on structural breaks focuses on ex post identification of break points that may have occurred in the past. While this question is important, a more challenging problem facing econometricians is to provide forecasts when the data generating process is unstable. The purpose of this paper is to provide a general methodology for forecasting in the presence of model instability. We make no assumptions on the number of break points or the law of motion governing parameter changes. Our approach makes use of Bayesian methods of model comparison and learning in order to provide an optimal predictive density from which forecasts can be derived. Estimates for the posterior probability that a break occurred at a particular point in the sample are generated as a byproduct of our procedure. We discuss the importance of using priors that accurately reflect the econometrician's opinions as to what constitutes a plausible forecast. Several applications to macroeconomic time-series data demonstrate the usefulness of our procedure.
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Paper provided by CIRPEE in its series Cahiers de recherche with number
0422.
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Date of creation: 2004Date of revision:
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Keywords: Bayesian Model Averaging Markov Chain Monte Carlo Real GDP Growth Phillip's Curve Other versions of this item:
Find related papers by JEL classification: C5 - Mathematical and Quantitative Methods - - Econometric Modeling
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references Cited by : (explanations , 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.)
Todd E. Clark & Michael W. McCracken, 2004.
"Improving forecast accuracy by combining recursive and rolling forecasts ,"
Research Working Paper
RWP 04-10, Federal Reserve Bank of Kansas City.
[Downloadable!]
Other versions: Pesaran, M.H. & Pettenuzzo, D. & Timmermann, A., 2004.
"‘Forecasting Time Series Subject to Multiple Structural Breaks’ ,"
Cambridge Working Papers in Economics
0433, Faculty of Economics, University of Cambridge.
[Downloadable!]
Other versions:
Pesaran, M. Hashem & Pettenuzzo, Davide & Timmermann, Allan, 2004.
"Forecasting Time Series Subject to Multiple Structural Breaks ,"
IZA Discussion Papers
1196, Institute for the Study of Labor (IZA).
[Downloadable!] Pesaran, M Hashem & Pettenuzzo, Davide & Timmermann, Allan G, 2004.
"Forecasting Time Series Subject to Multiple Structural Breaks ,"
CEPR Discussion Papers
4636, C.E.P.R. Discussion Papers.
[Downloadable!] (restricted) M. Hashem Pesaran & Davide Pettenuzzo & Allan Timmermann, 2004.
"Forecasting Time Series Subject to Multiple Structural Breaks ,"
CESifo Working Paper Series
CESifo Working Paper No. , CESifo GmbH.
[Downloadable!] Hashem Pesaran & Davide Pettenuzzo & Allan Timmermann, 2006.
"Forecasting Time Series Subject to Multiple Structural Breaks ,"
Review of Economic Studies ,
Blackwell Publishing, vol. 73(4), pages 1057-1084, October.
[Downloadable!] (restricted) Todd E. Clark & Michael W. McCracken, 2006.
"Forecasting of small macroeconomic VARs in the presence of instabilities ,"
Research Working Paper
RWP 06-09, Federal Reserve Bank of Kansas City.
[Downloadable!]
Zhongfang He & John M Maheu, 2008.
"Real Time Detection of Structural Breaks in GARCH Models ,"
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"Forecasting and Estimating Multiple Change-point Models with an Unknown Number of Change-points ,"
Discussion Papers in Economics
04/31, Department of Economics, University of Leicester.
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"How useful are historical data for forecasting the long-run equity return distribution? ,"
Working Papers
tecipa-293, University of Toronto, Department of Economics.
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Other versions:
John M. Maheu & Thomas H. McCurdy, 2007.
"How useful are historical data for forecasting the long-run equity return distribution? ,"
Working Paper Series
19-07, Rimini Centre for Economic Analysis, revised Jul 2007.
[Downloadable!] Maheu, John M. & McCurdy, Thomas H., 2009.
"How Useful are Historical Data for Forecasting the Long-Run Equity Return Distribution? ,"
Journal of Business & Economic Statistics ,
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"On the Evolution of Monetary Policy ,"
Working Paper Series
24-08, Rimini Centre for Economic Analysis, revised Jan 2008.
[Downloadable!]
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