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A New Application of Exact Nonparametric Methods to Long-Horizon Predictability Tests

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Author Info
Wei Liu (University of Toronto)
Alex Maynard (School of Business & Economics, Wilfrid Laurier University)

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Abstract

Empirical results from long-horizon regression tests have been influential in the finance literature. Yet, it has come to be understood that traditional long-horizon tests may be unreliable in finite samples when regressors are persistent and when the horizon is long relative to sample size. Recent research has provided valid alternative inference procedures in long-horizon regression in the case for which the regressor follows a near-unit root autoregressive process. However, in small samples, such processes may sometimes be difficult to distinguish with confidence from other persistent data generating processes, such as those displaying long-memory or structural breaks. In this paper, we demonstrate a simple means by which existing nonparametric sign and signed rank tests may be applied to provide exact inference in long-horizon predictive tests, without requiring any modeling assumptions on the regressor. Employing this robust approach, we find evidence of stock return predictability at moderate horizons using short-term interest rates, but little evidence of either short or long-run predictability using dividend-price ratios.

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Publisher Info
Article provided by Berkeley Electronic Press in its journal Studies in Nonlinear Dynamics & Econometrics.

Volume (Year): 11 (2007)
Issue (Month): 1 ()
Pages: 1376-1376
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Handle: RePEc:bep:sndecm:11:2007:1:1376-1376

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Related research
Keywords: sign test signed rank test long-horizon regression predictive regression structural breaks long-memory

References listed on IDEAS
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