Add to ORKGIn 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\u2013153). 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 issu...
We consider inference for linear regression models estimated by weighted-average least squares (WALS...
We consider model selection uncertainty in linear regression. We study theoretically and by simulati...
The method of model averaging has become an important tool to deal with model uncertainty, for examp...
In this article, we describe the estimation of linear regression models with uncertainty about the c...
In this article, we describe the estimation of linear regression models with uncertainty about the c...
In this article, we describe the estimation of linear regression models with uncertainty about the c...
Standard statistical practice ignores model uncertainty. Data analysts typically select a model from...
Standard statistical practice ignores model uncertainty. Data analysts typically select a model from...
Abstract. Standard statistical practice ignores model uncertainty. Data analysts typically select a ...
Parameter estimation under model uncertainty is a difficult and fundamental issue in econometrics. T...
Empirical growth research faces a high degree of model uncertainty. Apart from the neoclassical grow...
Linear regression analyses commonly involve two consecutive stages of statistical inquiry. In the fi...
This paper develops the theoretical background for the Limited Information Bayesian Model Averaging ...
The standard practice of selecting a single model from some class of models and then making inferenc...
The method of model averaging has become an important tool to deal with model uncertainty, in parti...
We consider inference for linear regression models estimated by weighted-average least squares (WALS...
We consider model selection uncertainty in linear regression. We study theoretically and by simulati...
The method of model averaging has become an important tool to deal with model uncertainty, for examp...
In this article, we describe the estimation of linear regression models with uncertainty about the c...
In this article, we describe the estimation of linear regression models with uncertainty about the c...
In this article, we describe the estimation of linear regression models with uncertainty about the c...
Standard statistical practice ignores model uncertainty. Data analysts typically select a model from...
Standard statistical practice ignores model uncertainty. Data analysts typically select a model from...
Abstract. Standard statistical practice ignores model uncertainty. Data analysts typically select a ...
Parameter estimation under model uncertainty is a difficult and fundamental issue in econometrics. T...
Empirical growth research faces a high degree of model uncertainty. Apart from the neoclassical grow...
Linear regression analyses commonly involve two consecutive stages of statistical inquiry. In the fi...
This paper develops the theoretical background for the Limited Information Bayesian Model Averaging ...
The standard practice of selecting a single model from some class of models and then making inferenc...
The method of model averaging has become an important tool to deal with model uncertainty, in parti...
We consider inference for linear regression models estimated by weighted-average least squares (WALS...
We consider model selection uncertainty in linear regression. We study theoretically and by simulati...
The method of model averaging has become an important tool to deal with model uncertainty, for examp...