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Long-Horizon Regressions: Theoretical Results and Applications to the Expected Returns/Dividend Yields and Fisher Effect Relations

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  • Valkanov, Rossen
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    Abstract

    We analyze several ways of conducting long-horizon regressions, taken from the empirical literature. Asymptotic arguments are used to show that, in all cases, the t-statistics do not converge to well-defined distributions, thus explaining the tendency of long-horizon regressions to find ‘significant†results, where previous short-term approaches have failed. Moreover, in some cases, the ordinary least squares estimator is not consistent, and the R^2 cannot be interpreted as a measure of the goodness of fit. Those results cast doubt on the conclusions reached by most previous long-horizon regression studies. We propose a rescaled t-statistic, whose asymptotic distribution is easy to simulate, and re-visit some of the evidence on the long-horizon predictability of returns and the long-horizon tests of the Fisher Effect.

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    Paper provided by Anderson Graduate School of Management, UCLA in its series University of California at Los Angeles, Anderson Graduate School of Management with number qt67b2h2gb.

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    Date of creation: 09 Sep 1999
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    Handle: RePEc:cdl:anderf:qt67b2h2gb

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    1. James H. Stock & Mark W. Watson, 1991. "A simple estimator of cointegrating vectors in higher order integrated systems," Working Paper Series, Macroeconomic Issues 91-3, Federal Reserve Bank of Chicago.
    2. Fisher, Mark E & Seater, John J, 1993. "Long-Run Neutrality and Superneutrality in an ARIMA Framework," American Economic Review, American Economic Association, vol. 83(3), pages 402-15, June.
    3. Cavanagh, Christopher L. & Elliott, Graham & Stock, James H., 1995. "Inference in Models with Nearly Integrated Regressors," Econometric Theory, Cambridge University Press, vol. 11(05), pages 1131-1147, October.
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    5. Campbell, John Y., 2001. "Why long horizons? A study of power against persistent alternatives," Journal of Empirical Finance, Elsevier, vol. 8(5), pages 459-491, December.
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    8. Granger, C. W. J. & Newbold, P., 1974. "Spurious regressions in econometrics," Journal of Econometrics, Elsevier, vol. 2(2), pages 111-120, July.
    9. Fama, Eugene F. & French, Kenneth R., 1988. "Dividend yields and expected stock returns," Journal of Financial Economics, Elsevier, vol. 22(1), pages 3-25, October.
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    11. Hansen, Bruce E., 1992. "Convergence to Stochastic Integrals for Dependent Heterogeneous Processes," Econometric Theory, Cambridge University Press, vol. 8(04), pages 489-500, December.
    12. Mishkin, Frederic S., 1990. "What does the term structure tell us about future inflation?," Journal of Monetary Economics, Elsevier, vol. 25(1), pages 77-95, January.
    13. Goetzmann, W.N., 1990. "Testing The Predictive Power Of Dividend Yields," Papers fb-_90-12, Columbia - Graduate School of Business.
    14. Peter C.B. Phillips, 1985. "Understanding Spurious Regressions in Econometrics," Cowles Foundation Discussion Papers 757, Cowles Foundation for Research in Economics, Yale University.
    15. John Y. Campbell & John Cochrane, 1999. "Force of Habit: A Consumption-Based Explanation of Aggregate Stock Market Behavior," Journal of Political Economy, University of Chicago Press, vol. 107(2), pages 205-251, April.
    16. Valkanov, Rossen, 1999. "Equity Premium and Dividend Yield regressions: A lot of noise, little information, confusing results," University of California at Los Angeles, Anderson Graduate School of Management qt955135m1, Anderson Graduate School of Management, UCLA.
    17. Nelson, Charles R & Kim, Myung J, 1993. " Predictable Stock Returns: The Role of Small Sample Bias," Journal of Finance, American Finance Association, vol. 48(2), pages 641-61, June.
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