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Forecasting in the presence of level shifts

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  • Aaron Smith

    (University of California, USA)

Abstract

This article addresses the problem of forecasting time series that are subject to level shifts. Processes with level shifts possess a nonlinear dependence structure. Using the stochastic permanent breaks (STOPBREAK) model, I model this nonlinearity in a direct and flexible way that avoids imposing a discrete regime structure. I apply this model to the rate of price inflation in the United States, which I show is subject to level shifts. These shifts significantly affect the accuracy of out-of-sample forecasts, causing models that assume covariance stationarity to be substantially biased. Models that do not assume covariance stationarity, such as the random walk, are unbiased but lack precision in periods without shifts. I show that the STOPBREAK model outperforms several alternative models in an out-of-sample inflation forecasting experiment. Copyright © 2005 John Wiley & Sons, Ltd.

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File URL: http://hdl.handle.net/10.1002/for.968
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Bibliographic Info

Article provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.

Volume (Year): 24 (2005)
Issue (Month): 8 ()
Pages: 557-574

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Handle: RePEc:jof:jforec:v:24:y:2005:i:8:p:557-574

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Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966

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  1. Andrews, Donald W. K. & Lee, Inpyo & Ploberger, Werner, 1996. "Optimal changepoint tests for normal linear regression," Journal of Econometrics, Elsevier, vol. 70(1), pages 9-38, January.
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Cited by:
  1. repec:hal:cesptp:halshs-00658540 is not listed on IDEAS
  2. Barbara Rossi, 2011. "Advances in Forecasting Under Instability," Working Papers 11-20, Duke University, Department of Economics.
  3. Michael D. Bradley & Dennis W. Jansen & Tara M. Sinclair, 2013. "How Well Does "Core" Inflation Capture Permanent Price Changes?," Working Papers 2013-4, The George Washington University, Institute for International Economic Policy.
  4. repec:hal:journl:halshs-00658540 is not listed on IDEAS

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