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Confidence intervals for long-horizon predictive regressions via reverse regressions

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  • Min Wei
  • Jonathan Wright
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Abstract

Long-horizon predictive regressions in finance pose formidable econometric problems when estimated using the sample sizes that are typically available. A remedy that has been proposed by Hodrick (1992) is to run a reverse regression in which short-horizon returns are projected onto a long-run mean of some predictor. By covariance stationarity, the slope coefficient is zero in the reverse regression if and only if it is zero in the original regression, but testing the hypothesis in the reverse regression avoids small sample problems. Unfortunately this only allows us to test the null of no predictability. In this paper we show how to use the reverse regression to test other hypotheses about the slope coefficient in a long-horizon predictive regression, and to form confidence intervals for this coefficient. We show that this approach to inference works well in small samples.

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Bibliographic Info

Paper provided by Board of Governors of the Federal Reserve System (U.S.) in its series Finance and Economics Discussion Series with number 2009-27.

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Date of creation: 2009
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Handle: RePEc:fip:fedgfe:2009-27

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Related research

Keywords: Regression analysis ; Stocks;

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References

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  1. Campbell, John, 2000. "Asset Pricing at the Millennium," Scholarly Articles 3294737, Harvard University Department of Economics.
  2. John Y. Campbell & Motohiro Yogo, 2002. "Efficient Tests of Stock Return Predictability," Harvard Institute of Economic Research Working Papers 1972, Harvard - Institute of Economic Research.
  3. Geert Bekaert & Robert J. Hodrick & David A. Marshall, 1997. ""Peso Problem" Explanations for Term Structure Anomalies," NBER Working Papers 6147, National Bureau of Economic Research, Inc.
  4. Elliott, Graham & Stock, James H., 1994. "Inference in Time Series Regression When the Order of Integration of a Regressor is Unknown," Econometric Theory, Cambridge University Press, vol. 10(3-4), pages 672-700, August.
  5. Goetzman, W.N. & Jorion, P., 1992. "Testing the Predictive Power of Dividend Yields," Papers 93-03, Columbia - Graduate School of Business.
  6. 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.
  7. Hansen, Lars Peter & Hodrick, Robert J, 1980. "Forward Exchange Rates as Optimal Predictors of Future Spot Rates: An Econometric Analysis," Journal of Political Economy, University of Chicago Press, vol. 88(5), pages 829-53, October.
  8. Robert F. Stambaugh, 1999. "Predictive Regressions," NBER Technical Working Papers 0240, National Bureau of Economic Research, Inc.
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Cited by:
  1. Bakshi, Gurdip & Panayotov, George, 2013. "Predictability of currency carry trades and asset pricing implications," Journal of Financial Economics, Elsevier, vol. 110(1), pages 139-163.
  2. Bakshi, Gurdip & Panayotov, George & Skoulakis, Georgios, 2011. "Improving the predictability of real economic activity and asset returns with forward variances inferred from option portfolios," Journal of Financial Economics, Elsevier, vol. 100(3), pages 475-495, June.

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