Finance and Economics Discussion Series (FEDS)
June 2009
Confidence Intervals for Long-Horizon Predictive Regressions via Reverse Regressions
Min Wei and Jonathan Wright
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 the null of no predictability to be tested. 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 hence to form confidence intervals for this coefficient. We show that this approach to inference works well in small samples, even when the predictors are highly persistent.
Full paper (Screen Reader Version)Keywords: Predictive regressions, long horizons, confidence intervals, small sample problems, persistence
PDF: Full Paper
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