Weighted-average least squares: Beyond the classical linear regression model

Abstract

This paper introduces four new commands for the weighted-average least squares approach to model uncertainty: the hetwals command fits linear models with multiplicative forms of heteroskedasticity; the ar1wals command fits linear models with stationary AR(1) errors; the xtwals command fits fixed-effects and random-effects panel-data models with either i.i.d. or AR(1) idiosyncratic errors; while the glmwals command fits univariate generalized linear models. These commands extend the new functionalities of the wals command (version 3.0) introduced by De Luca and Magnus (2025a), and enlarge the classes of models that can be fitted by this model-averaging method. We also provide an illustration of the hetwals and glmwals commands by means of real data applications.