IDEAS home Printed from https://ideas.repec.org/a/jof/jforec/v24y2005i8p557-574.html
   My bibliography  Save this article

Forecasting in the presence of level shifts

Author

Listed:
  • 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.

Suggested Citation

  • Aaron Smith, 2005. "Forecasting in the presence of level shifts," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(8), pages 557-574.
    • Handle: RePEc:jof:jforec:v:24:y:2005:i:8:p:557-574
    DOI: 10.1002/for.968
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1002/for.968
    File Function: Link to full text; subscription required
    Download Restriction: no
    File URL: https://libkey.io/10.1002/for.968?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Dick van Dijk & Timo Terasvirta & Philip Hans Franses, 2002. "Smooth Transition Autoregressive Models — A Survey Of Recent Developments," Econometric Reviews, Taylor & Francis Journals, vol. 21(1), pages 1-47.
    2. Robert J. Gordon, 1997. "The Time-Varying NAIRU and Its Implications for Economic Policy," Journal of Economic Perspectives, American Economic Association, vol. 11(1), pages 11-32, Winter.
    3. Timmermann, Allan, 2001. "Structural Breaks, Incomplete Information, and Stock Prices," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(3), pages 299-314, July.
    4. West, Kenneth D, 1996. "Asymptotic Inference about Predictive Ability," Econometrica, Econometric Society, vol. 64(5), pages 1067-1084, September.
    5. 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.
    6. Christoffersen, Peter F. & Diebold, Francis X., 1997. "Optimal Prediction Under Asymmetric Loss," Econometric Theory, Cambridge University Press, vol. 13(6), pages 808-817, December.
    7. Jushan Bai & Pierre Perron, 1998. "Estimating and Testing Linear Models with Multiple Structural Changes," Econometrica, Econometric Society, vol. 66(1), pages 47-78, January.
    8. Robert F. Engle & Aaron D. Smith, 1999. "Stochastic Permanent Breaks," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 553-574, November.
    9. 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.
    10. Hansen, Bruce E, 1996. "Inference When a Nuisance Parameter Is Not Identified under the Null Hypothesis," Econometrica, Econometric Society, vol. 64(2), pages 413-430, March.
    11. Stock, James H & Watson, Mark W, 1996. "Evidence on Structural Instability in Macroeconomic Time Series Relations," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(1), pages 11-30, January.
    12. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    13. Chen, Chung & Tiao, George C, 1990. "Random Level-Shift Time Series Models, ARIMA Approximations, and Level-Shift Detection," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(1), pages 83-97, January.
    14. Chib, Siddhartha, 1998. "Estimation and comparison of multiple change-point models," Journal of Econometrics, Elsevier, vol. 86(2), pages 221-241, June.
    15. Harvey, Andrew, 1997. "Trends, Cycles and Autoregressions," Economic Journal, Royal Economic Society, vol. 107(440), pages 192-201, January.
    16. Clements, Michael P & Hendry, David F, 1996. "Intercept Corrections and Structural Change," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(5), pages 475-494, Sept.-Oct.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Rossi, Barbara, 2013. "Advances in Forecasting under Instability," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1203-1324, Elsevier.
    2. Mathieu Gatumel & Florian Ielpo, 2014. "The Number Of Regimes Across Asset Returns: Identification And Economic Value," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 17(06), pages 1-25.
    3. Bradley, Michael D. & Jansen, Dennis W. & Sinclair, Tara M., 2015. "How Well Does “Core” Inflation Capture Permanent Price Changes?," Macroeconomic Dynamics, Cambridge University Press, vol. 19(4), pages 791-815, June.
    4. Mathieu Gatumel & Florian Ielpo, 2011. "The Number of Regimes Across Asset Returns: Identification and Economic Value," Post-Print halshs-00658540, HAL.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rossi, Barbara, 2013. "Advances in Forecasting under Instability," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1203-1324, Elsevier.
    2. Pesaran, M. Hashem & Timmermann, Allan, 2004. "How costly is it to ignore breaks when forecasting the direction of a time series?," International Journal of Forecasting, Elsevier, vol. 20(3), pages 411-425.
    3. John M. Maheu & Stephen Gordon, 2008. "Learning, forecasting and structural breaks," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(5), pages 553-583.
    4. Smith, Aaron, 2005. "Level Shifts and the Illusion of Long Memory in Economic Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 321-335, July.
    5. Giorgio Valente & Lucio Sarno, 2005. "Modelling and forecasting stock returns: exploiting the futures market, regime shifts and international spillovers," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(3), pages 345-376.
    6. Smith Aaron, 2012. "Markov Breaks in Regression Models," Journal of Time Series Econometrics, De Gruyter, vol. 4(1), pages 1-35, May.
    7. Hendry, David F. & Clements, Michael P., 2003. "Economic forecasting: some lessons from recent research," Economic Modelling, Elsevier, vol. 20(2), pages 301-329, March.
    8. Richard H. Clarida & Lucio Sarno & Mark P. Taylor & Giorgio Valente, 2006. "The Role of Asymmetries and Regime Shifts in the Term Structure of Interest Rates," The Journal of Business, University of Chicago Press, vol. 79(3), pages 1193-1224, May.
    9. Corradi, Valentina & Swanson, Norman R., 2004. "Some recent developments in predictive accuracy testing with nested models and (generic) nonlinear alternatives," International Journal of Forecasting, Elsevier, vol. 20(2), pages 185-199.
    10. Terasvirta, Timo, 2006. "Forecasting economic variables with nonlinear models," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 8, pages 413-457, Elsevier.
    11. Singh, Tarlok, 2014. "On the regime-switching and asymmetric dynamics of economic growth in the OECD countries," Research in Economics, Elsevier, vol. 68(2), pages 169-192.
    12. Charfeddine, Lanouar & Guégan, Dominique, 2012. "Breaks or long memory behavior: An empirical investigation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(22), pages 5712-5726.
    13. Bisaglia, Luisa & Gerolimetto, Margherita, 2008. "Forecasting long memory time series when occasional breaks occur," Economics Letters, Elsevier, vol. 98(3), pages 253-258, March.
    14. Lanouar Charfeddine & Dominique Guegan, 2009. "Breaks or Long Memory Behaviour: An empirical Investigation," Post-Print halshs-00377485, HAL.
    15. Barbara Rossi, 2019. "Forecasting in the presence of instabilities: How do we know whether models predict well and how to improve them," Economics Working Papers 1711, Department of Economics and Business, Universitat Pompeu Fabra, revised Jul 2021.
    16. Geweke, John & Jiang, Yu, 2011. "Inference and prediction in a multiple-structural-break model," Journal of Econometrics, Elsevier, vol. 163(2), pages 172-185, August.
    17. Dufays, Arnaud & Rombouts, Jeroen V.K., 2020. "Relevant parameter changes in structural break models," Journal of Econometrics, Elsevier, vol. 217(1), pages 46-78.
    18. Akhter Faroque & William Veloce & Jean-Francois Lamarche, 2012. "Have structural changes eliminated the out-of-sample ability of financial variables to forecast real activity after the mid-1980s? Evidence from the Canadian economy," Applied Economics, Taylor & Francis Journals, vol. 44(30), pages 3965-3985, October.
    19. Travaglini, Guido, 2007. "The U.S. Dynamic Taylor Rule With Multiple Breaks, 1984-2001," MPRA Paper 3419, University Library of Munich, Germany, revised 15 Jun 2007.
    20. Mehmet Balcilar & Rangan Gupta & Anandamayee Majumdar & Stephen M. Miller, 2012. "Was the Recent Downturn in US GDP Predictable?," Working Papers 1210, University of Nevada, Las Vegas , Department of Economics.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:jof:jforec:v:24:y:2005:i:8:p:557-574. See general information about how to correct material in RePEc.

    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 CitEc recognized a bibliographic reference but did not link an item in RePEc 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 RePEc Author Service profile, as there may be some citations waiting for confirmation.

    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 (email available below). General contact details of provider: http://www3.interscience.wiley.com/cgi-bin/jhome/2966 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.