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

Giuseppe De Luca and Jan Magnus

Stata Journal, 2025, vol. 25, issue 4, 772-811

Abstract: In this article, we introduce 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 first-order autoregressive errors; the xtwals command fits fixed-effects and random-effects panel-data models with ei- ther independent and identically distributed or first-order autoregressive idiosyn- cratic errors; and the glmwals command fits univariate generalized linear mod- els. These commands extend the new functionalities of the wals command (ver- sion 3.0), introduced by De Luca and Magnus (2025, Stata Journal 25: 587–626), and enlarge the classes of models that can be fit by this model-averaging method. We also illustrate the hetwals and glmwals commands via real-data applications.

Keywords: hetwals; ar1wals; xtwals; glmwals; postestimation; weighted- average least squares; heteroskedasticity; serial correlation; panel data; generalized linear models (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1177/1536867X251398599

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