6533b855fe1ef96bd12b0042
RESEARCH PRODUCT
Bayesian model averaging and weighted-average least squares: Equivariance, stability, and numerical issues
Jan R. MagnusGiuseppe De Lucasubject
Mathematical optimizationWalsBayesian probabilityStability (learning theory)Bayesian analysisSettore SECS-P/05 - EconometriaInferenceBmaBayesian inference01 natural sciencesLeast squares010104 statistics & probabilityMathematics (miscellaneous)st0239 bma wals model uncertainty model averaging Bayesian analysis exact Bayesian model averaging weighted-average least squares0502 economics and businessLinear regressionWeighted-average least squares0101 mathematicsSettore SECS-P/01 - Economia Politica050205 econometrics Mathematicsst0239Exact bayesian model averagingModel selection05 social sciencesEstimatorModel uncertaintyAlgorithmModel averagingdescription
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 Prüfer (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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2011-12-01 |