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

Author

Listed:
  • Giuseppe De Luca

    (University of Palermo)

  • Jan R. Magnus

    (Vrije Universiteit Amsterdam)

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.

Suggested Citation

  • Giuseppe De Luca & Jan R. Magnus, 2025. "Weighted-average least squares: Beyond the classical linear regression model," Stata Journal, StataCorp LLC, vol. 25(4), pages 772-811, December.
    • Handle: RePEc:tsj:stataj:v:25:y:2025:i:4:p:772-811
    DOI: 10.1177/1536867X251398599
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