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A simple approach for diagnosing instabilities in predictive regressions

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  • Pitarakis, Jean-Yves

Abstract

We introduce a method for detecting the presence of time variation and instabilities in the parameters of predictive regressions linking noisy variables such as stock returns to highly persistent predictors such as stock market valuation ratios. Our proposed approach relies on the least squares based squared residuals of the predictive regression and is trivial to implement. More importantly the distribution of our test statistic is shown to be free of nuisance parameters, is already tabulated in the literature and is robust to the degree of persistence of the chosen predictor. Our proposed method is subsequently applied to the predictability of monthly US stock returns with the dividend yield, dividend payout, earnings-price, dividend-price and book-to-market value ratios. Our results strongly support the presence of instabilities over the 1927-2013 period but also clearly point to the disappearance of these after the mid 50s. Keywords; predictability of stock returns, structural breaks, CUSUMSQ, predictive regressions

Suggested Citation

  • Pitarakis, Jean-Yves, 2015. "A simple approach for diagnosing instabilities in predictive regressions," Discussion Paper Series In Economics And Econometrics 1519, Economics Division, School of Social Sciences, University of Southampton.
  • Handle: RePEc:stn:sotoec:1519
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    1. Ploberger, Werner & Krämer;, Walter, 1990. "The Local Power of the CUSUM and CUSUM of Squares Tests," Econometric Theory, Cambridge University Press, vol. 6(03), pages 335-347, September.
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    4. Jean-Yves Pitarakis, 2004. "Least squares estimation and tests of breaks in mean and variance under misspecification," Econometrics Journal, Royal Economic Society, vol. 7(1), pages 32-54, June.
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    12. David E. Rapach & Mark E. Wohar, 2006. "Structural Breaks and Predictive Regression Models of Aggregate U.S. Stock Returns," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 4(2), pages 238-274.
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