Weighted-average least squares: Improvements and extensions

This article presents version 3.0 of the wals command, which implements the weighted-average least-squares estimator of Magnus, Powell, and Prüfer (2010, Journal of Econometrics 154: 139–153). Version 3.0 improves earlier 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.

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Publication Details

Journal
The Stata Journal Promoting communications on statistics and Stata
Published
2025-08-26
DOI
https://doi.org/10.1177/1536867x251365494
Citations
1
Primary Topic
Advanced Statistical Methods and Models
Type
article
Field-Weighted Citation Impact
2.63
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article

Weighted-average least squares: Improvements and extensions

Giuseppe De Luca, Jan R. Magnus
1 citations
The Stata Journal Promoting communications on statistics and Stata
Advanced Statistical Methods and Models
2.63
article

Weighted-average least squares: Improvements and extensions

Giuseppe De Luca, Jan R. Magnus
article en
1 citations

Abstract

This article presents version 3.0 of the wals command, which implements the weighted-average least-squares estimator of Magnus, Powell, and Prüfer (2010, Journal of Econometrics 154: 139–153). Version 3.0 improves earlier 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.

The Stata Journal Promoting communications on statistics and StataVol. 25(3)
Vrije Universiteit Amsterdam (NL), University of Palermo (IT)
Openalex Percentile: Top 9%
Advanced Statistical Methods and Models
2.63
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Weighted-average least squares: Improvements and extensions — Giuseppe De Luca, Jan R. Magnus · The Stata Journal Promoting communications on statistics and Stata (2025) | TGRS Research Map | TGRS