Predictive Regressions: Method of Least Autocorrelation

42 Pages Posted: 8 Sep 2006

Date Written: September 6, 2006

Abstract

Conventional predictive regressions produce biased and inefficient coefficient estimates in small samples when the predicting variable is Gaussian first order persistent and its innovations are highly correlated with the error series of the return. We propose a new estimation method (the method of least autocorrelation) conditional on the assumption of serially uncorrelated innovations to the dependent variable. The new estimator minimizes the weighted sum of squared autocorrelations of residuals obtained from fitting a return series through estimated parameters. The asymptotic properties of the estimator and a refined weighting matrix are discussed. Finite sample behavior of the estimator is compared with existing estimation methods in the simulation study. The new method produces more accurate and efficient point estimate of the true parameter and improves performance in the power and size tests. Our empirical results provide some fresh evidence on the predictability of stock returns during the post-war time period.

Keywords: Predictability, Method of Least Autocorrelation, Hypothesis Testing, Estimation

JEL Classification: C12, C13, C32, G12

Suggested Citation

Zhu, John Qi and Liu, Yan, Predictive Regressions: Method of Least Autocorrelation (September 6, 2006). Available at SSRN: https://ssrn.com/abstract=928877 or http://dx.doi.org/10.2139/ssrn.928877

Yan Liu

Emory University ( email )

201 Dowman Drive
Atlanta, GA 30322
United States

No contact information is available for John Qi Zhu

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