In this article, we describe the estimation of linear regression models with uncertainty about the choice of the explanatory variables. We introduce the Stata commands bma and wals, which implement, respectively, the exact Bayesian model-averaging estimator and the weighted-average least-squares estimator developed by Magnus, Powell, and Prufer (2010, Journal of Econometrics 154: 139–153). Unlike standard pretest estimators that are based on some preliminary diagnostic test, these model-averaging estimators provide a coherent way of making inference on the regression parameters of interest by taking into account the uncertainty due to both the estimation and the model selection steps. Special emphasis is given to several practical issues that users are likely to face in applied work: equivariance to certain transformations of the explanatory variables, stability, accuracy, computing speed, and out-of-memory problems. Performances of our bma and wals commands are illustrated using simulated data and empirical applications from the literature on model-averaging estimation.

De Luca, G., Magnus, J.R. (2011). Bayesian model averaging and weighted-average least squares: equivariance, stability, and numerical issues. THE STATA JOURNAL, 11(4), 518-544.

Bayesian model averaging and weighted-average least squares: equivariance, stability, and numerical issues

DE LUCA, Giuseppe;
2011-01-01

Abstract

In this article, we describe the estimation of linear regression models with uncertainty about the choice of the explanatory variables. We introduce the Stata commands bma and wals, which implement, respectively, the exact Bayesian model-averaging estimator and the weighted-average least-squares estimator developed by Magnus, Powell, and Prufer (2010, Journal of Econometrics 154: 139–153). Unlike standard pretest estimators that are based on some preliminary diagnostic test, these model-averaging estimators provide a coherent way of making inference on the regression parameters of interest by taking into account the uncertainty due to both the estimation and the model selection steps. Special emphasis is given to several practical issues that users are likely to face in applied work: equivariance to certain transformations of the explanatory variables, stability, accuracy, computing speed, and out-of-memory problems. Performances of our bma and wals commands are illustrated using simulated data and empirical applications from the literature on model-averaging estimation.
2011
Settore SECS-P/01 - Economia Politica
Settore SECS-P/05 - Econometria
De Luca, G., Magnus, J.R. (2011). Bayesian model averaging and weighted-average least squares: equivariance, stability, and numerical issues. THE STATA JOURNAL, 11(4), 518-544.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/79310
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