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Model Instability and Choice of Observation Window

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Author Info
Hashem Pesaran (University of Cambridge)
Allan Timmermann (University of California, San Diego)

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Abstract

Recent evidence suggests that many economic time series are subject to structural breaks. In the presence of breaks, including historical data prior to the most recent break to estimate a forecasting model will lead to prediction errors that are biased but also may have a smaller variance. This paper examines the trade-off between the bias and variance of forecast errors and proposes a new set of reversed Cusum procedures to determine the window size that minimizes mean squared forecast error. This window size varies over time and depends on the size of the break, the distance to the break and the squared correlation coefficient between predicted and realized values. The forecasting performances of several procedures for determination of window size are compared in a simulation experiment and in a recursive prediction exercise using data on US stock returns. We find evidence that out-of-sample forecasting performance can be improved by explicitly accounting for breaks and adopting the proposed method for optimally determining the window size

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File URL: http://repositories.cdlib.org/cgi/viewcontent.cgi?article=1199&context=ucsdecon
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Publisher Info
Paper provided by Department of Economics, UC San Diego in its series University of California at San Diego, Economics Working Paper Series with number 1999-19.

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Date of creation: 01 Sep 1999
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Handle: RePEc:cdl:ucsdec:1999-19

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Related research
Keywords: parameter instability; forecasting; expanding and rolling window; reversed Cusum or Cusum squared tests; multiple breaks; choice of observation window; predictability of US stock returns;

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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.:
  1. Gabriel Perez-Quiros & Allan Timmermann, 2000. "Firm Size and Cyclical Variations in Stock Returns," Journal of Finance, American Finance Association, vol. 55(3), pages 1229-1262, 06. [Downloadable!] (restricted)
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  2. Campbell, John Y., 1987. "Stock returns and the term structure," Journal of Financial Economics, Elsevier, vol. 18(2), pages 373-399, June. [Downloadable!] (restricted)
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  3. Keim, Donald B. & Stambaugh, Robert F., 1986. "Predicting returns in the stock and bond markets," Journal of Financial Economics, Elsevier, vol. 17(2), pages 357-390, December. [Downloadable!] (restricted)
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  4. Jushan Bai & Pierre Perron, 1998. "Estimating and Testing Linear Models with Multiple Structural Changes," Econometrica, Econometric Society, vol. 66(1), pages 47-78, January.
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  5. M. Hashem Pesaran & Allan Timmermann, 1995. "Predictability of Stock Returns: Robustness and Economic Significance," University of California at San Diego, Economics Working Paper Series 95-19, Department of Economics, UC San Diego.
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  6. Alogoskoufis, George S & Smith, Ron, 1991. "The Phillips Curve, the Persistence of Inflation, and the Lucas Critique: Evidence from Exchange-Rate Regimes," American Economic Review, American Economic Association, vol. 81(5), pages 1254-75, December. [Downloadable!] (restricted)
  7. Leitch, Gordon & Tanner, J Ernest, 1991. "Economic Forecast Evaluation: Profits versus the Conventional Error Measures," American Economic Review, American Economic Association, vol. 81(3), pages 580-90, June. [Downloadable!] (restricted)
  8. 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. [Downloadable!] (restricted)
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  9. Chu, Chia-Shang James & Stinchcombe, Maxwell & White, Halbert, 1996. "Monitoring Structural Change," Econometrica, Econometric Society, vol. 64(5), pages 1045-65, September. [Downloadable!] (restricted)
  10. BAI, Jushan & PERRON, Pierre, 1998. "Computation and Analysis of Multiple Structural-Change Models," Cahiers de recherche 9807, Universite de Montreal, Departement de sciences economiques. [Downloadable!]
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  11. Granger, C.W.J. & Pesaran, M. H., 1999. "Economic and Statistical Measures of Forecast Accuracy," Cambridge Working Papers in Economics 9910, Faculty of Economics, University of Cambridge. [Downloadable!]
  12. Cooley, Thomas F & Prescott, Edward C, 1976. "Estimation in the Presence of Stochastic Parameter Variation," Econometrica, Econometric Society, vol. 44(1), pages 167-84, January. [Downloadable!] (restricted)
  13. Allan Timmermann, 1998. "Structural Breaks, Incomplete Information and Stock Prices," FMG Discussion Papers dp311, Financial Markets Group. [Downloadable!] (restricted)
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  14. Garcia, Rene & Perron, Pierre, 1996. "An Analysis of the Real Interest Rate under Regime Shifts," The Review of Economics and Statistics, MIT Press, vol. 78(1), pages 111-25, February. [Downloadable!] (restricted)
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  15. Breen, William & Glosten, Lawrence R & Jagannathan, Ravi, 1989. " Economic Significance of Predictable Variations in Stock Index Returns," Journal of Finance, American Finance Association, vol. 44(5), pages 1177-89, December. [Downloadable!] (restricted)
  16. Andrews, Donald W K, 1993. "Tests for Parameter Instability and Structural Change with Unknown Change Point," Econometrica, Econometric Society, vol. 61(4), pages 821-56, July. [Downloadable!] (restricted)
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  17. Ploberger, Werner & Kramer, Walter & Kontrus, Karl, 1989. "A new test for structural stability in the linear regression model," Journal of Econometrics, Elsevier, vol. 40(2), pages 307-318, February. [Downloadable!] (restricted)
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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.)

  1. 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:
  2. Filippo Altissimo & Valentina Corradi, 2000. "Strong Rules for Detecting the Number of Breaks in a Time Series," Econometric Society World Congress 2000 Contributed Papers 0574, Econometric Society. [Downloadable!]
    Other versions:
  3. Marcelle Chauvet, 2000. "Leading Indicators of Inflation for Brazil," Working Papers Series 7, Central Bank of Brazil, Research Department. [Downloadable!]
  4. Yang Yang & Tae-Hwy Lee, 2004. "Bagging Binary Predictors for Time Series," Econometric Society 2004 Far Eastern Meetings 512, Econometric Society. [Downloadable!]
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