Weighted average least squares estimation with nonspherical disturbances and an application to the Hong Kong housing market

Abstract

The recently proposed ‘weighted average least squares’ (WALS) estimator is a Bayesian combination of frequentist estimators. It has been shown that the WALS estimator possesses major advantages over standard Bayesian model averaging (BMA) estimators: the WALS estimator has bounded risk, allows a coherent treatment of ignorance and its computational effort is negligible. However, the sampling properties of the WALS estimator as compared to BMA estimators are heretofore unexamined. The WALS theory is further extended to allow for nonspherical disturbances, and the estimator is illustrated with data from the Hong Kong real estate market. Monte Carlo evidence shows that the WALS estimator performs significantly better than standard BMA and pretest alternatives.