This paper presents version 3.0 of the wals command, which implements the weighted-average least squares estimator of Magnus et al. (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 plug-in 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 post-estimation commands: the predict command associated with wals; the lcwals command for estimating linear combinations of the parameters; and the margwals command for estimating smooth, possibly nonlinear, functions of the parameters at given values of regressors. Finally, we compare our new commands with two suites of Stata commands for tackling issues of model uncertainty.
Giuseppe De Luca, Jan R. Magnus (2025). Weighted-average least squares: Improvements and extensions. THE STATA JOURNAL.
Weighted-average least squares: Improvements and extensions
Giuseppe De Luca
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Membro del Collaboration Group
;
2025-01-01
Abstract
This paper presents version 3.0 of the wals command, which implements the weighted-average least squares estimator of Magnus et al. (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 plug-in 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 post-estimation commands: the predict command associated with wals; the lcwals command for estimating linear combinations of the parameters; and the margwals command for estimating smooth, possibly nonlinear, functions of the parameters at given values of regressors. Finally, we compare our new commands with two suites of Stata commands for tackling issues of model uncertainty.File | Dimensione | Formato | |
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