A decade ago Fama and French (1998) estimated that 40% variations in stock returns was predictable over horizons of 3-5 years, which they attributed to a mean reverting stationary component in prices. While it has been clear that the Depression and war years exert a strong influence on these estimates, it has not been clear whether the large returns of that period contribute to the information in the data or rather are a source of noise to be discounted in estimation. This paper uses the Gibbs-sampling-augmented randomization methodology to address the problem of heteroskedasticity in estimation of multi-period return autoregressions.
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Paper provided by University of Washington, Department of Economics in its series Working Papers with number
97-07.