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The Statistics Of Long-Horizon Regressions Revisited

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  • Jacob Boudouk
  • Matthew Richardson
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    Abstract

    This paper compares commonly used approaches for estimating the relation between long-horizon returns and a predetermined variable X 1, such as dividend yields. Specifically, we look at regression of (i) nonoverlapping multiperiod returns on X t (ii) overlapping multiperiod returns on X t, (iii) single-period returns on multiperiod X t, and (iv) single-period returns on X t and its implied long-horizon regression coefficient. We provide analytical formulae which quantify the efficiency of the estimators used in the various approaches. Using the formulae, as well as Monte Carlo simulations, we demonstrate that the relative efficiency of the estimators used in the various approaches differs remarkably, depending on the dynamic structure of the regressor. of special interest for financial economists, when the regressors are highly autocorrelated, we find that the regressions (ii) (iii), and (iv) provide only "marginal" efficiency gains above and beyond the nonoverlapping long-horizon regression. Copyright 1994 Blackwell Publishers.

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    File URL: http://www.blackwell-synergy.com/doi/abs/10.1111/j.1467-9965.1994.tb00052.x
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    Bibliographic Info

    Article provided by Wiley Blackwell in its journal Mathematical Finance.

    Volume (Year): 4 (1994)
    Issue (Month): 2 ()
    Pages: 103-119

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    Handle: RePEc:bla:mathfi:v:4:y:1994:i:2:p:103-119

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    Cited by:
    1. Jorion, Philippe, 1996. "Does real interest parity hold at longer maturities?," Journal of International Economics, Elsevier, vol. 40(1-2), pages 105-126, February.
    2. Lunde A. & Timmermann A., 2004. "Duration Dependence in Stock Prices: An Analysis of Bull and Bear Markets," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 253-273, July.
    3. Wayne E. Ferson & Sergei Sarkissian & Timothy Simin, 2006. "Asset Pricing Models with Conditional Betas and Alphas: The Effects of Data Snooping and Spurious Regression," NBER Working Papers 12658, National Bureau of Economic Research, Inc.
    4. Snaith, Stuart & Coakley, Jerry & Kellard, Neil, 2013. "Does the forward premium puzzle disappear over the horizon?," Journal of Banking & Finance, Elsevier, vol. 37(9), pages 3681-3693.
    5. Chinn, Menzie D. & Meredith, Guy, 2000. "Testing uncovered interest parity at short and long horizons," HWWA Discussion Papers 102, Hamburg Institute of International Economics (HWWA).
    6. Martin, Anna D. & Mauer, Laurence J., 2005. "A note on common methods used to estimate foreign exchange exposure," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 15(2), pages 125-140, April.
    7. Chinn, Menzie David & Meredith, Guy, 2000. "Interest parity at short and long horizons," SFB 373 Discussion Papers 2000,44, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    8. Chinn, Menzie D., 2006. "The (partial) rehabilitation of interest rate parity in the floating rate era: Longer horizons, alternative expectations, and emerging markets," Journal of International Money and Finance, Elsevier, vol. 25(1), pages 7-21, February.
    9. Ferson, Wayne E & Korajczyk, Robert A, 1995. "Do Arbitrage Pricing Models Explain the Predictability of Stock Returns?," The Journal of Business, University of Chicago Press, vol. 68(3), pages 309-49, July.
    10. Paul Harrison & Harold H. Zhang, . "Cyclical Variation in the Risk and Return Relation," Computing in Economics and Finance 1997 175, Society for Computational Economics.
    11. Jacob Boudoukh & Matthew Richardson & Robert Whitelaw, 2005. "The Myth of Long-Horizon Predictability," NBER Working Papers 11841, National Bureau of Economic Research, Inc.
    12. Berben, R-P. & van Dijk, D.J.C., 1998. "Does the absence of cointegration explain the typical findings in long horizon regressions?," Econometric Institute Research Papers EI 9814, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.

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