The Precision of Unconditional Estimators of the Equity Premium

Abstract

This paper has the purpose of providing unconditional estimators of the equity premium. In plain words the estimators are obtained by the constants in regressions of the equity premium on a constant. More than one specification is tried and more than one type of standard errors is implemented. The specifications include ordinary least squares, EGARCH, robust least squares, quantile regressions, and Markov switching regressions with two regimes. The analysis is repeated by adding in categorical variables that correspond to outliers. Theoretically these estimators of the equity premium are unbiased and consistent. All models are subjected to serial correlation tests on the residuals. These tests support the absence of serial correlation. This is conducive to the conclusion that the models are well specified and that the estimators are not only unbiased and consistent but also efficient. The paper gives point estimates and 95% confidence intervals of the equity premium, develops hypothesis tests, and reports point estimates of the standard errors. The results may help in assessing the magnitude of the equity premium and the precision with which this premium is measured.