Predictive regressions are subject to two small sample biases: the coefficient estimate is biased if the predictor is endogenous and asymptotic standard errors in the case of overlapping periods are biased downward. Both biases work in the direction of making t-ratios too large so that standard inference may indicate predictability even if none is present. Using annual returns since 1872 and monthly returns since 1927, the authors estimate empirical distributions by randomizing residuals in the vector autoregression representation of the variables. The estimated biases are large enough to affect inference in practice and should be accounted for when studying predictability. Copyright 1993 by American Finance Association.
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Article provided by American Finance Association in its journal Journal of Finance.
Volume (Year): 48 (1993) Issue (Month): 2 (June) Pages: 641-61 Download reference. The following formats are available: HTML,
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