Weighted-average least squares: Improvements and extensions

Giuseppe De Luca and Jan Magnus

Stata Journal, 2025, vol. 25, issue 3, 587-626

Abstract: This article presents version 3.0 of the wals command, which im- plements the weighted-average least-squares estimator of Magnus, Powell, and Prüfer (2010, Journal of Econometrics 154: 139–153). Version 3.0 improves ear- lier versions of wals in several respects: a new syntax supporting factor variables, time-series operators, and weights; an enlarged set of prior distributions; extended quadrature methods for computing the posterior mean; new plugin estimates of the sampling moments; simulation-based confidence intervals; and other options to control accuracy, computational speed, and output of wals. We also offer three new postestimation commands: the predict command associated with wals; the lcwals command, which estimates linear combinations of the parameters; and the margwals command, which estimates smooth, possibly nonlinear functions of the parameters at given values of regressors. Finally, we compare our new commands with two suites of commands for tackling issues of model uncertainty.

Keywords: wals; wals postestimation; lcwals; margwals; weighted-average least squares; linear model; Bayesian shrinkage; inference; postestimation (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1177/1536867X251365494

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