This paper generalizes the one-way error component model from the homoskedastic to the heteroskedastic case. Unlike P. Mazodier and A. Trognon's (1978) heteroskedastic two-way error component model, an explicit form for V11/2 is obtained that allows the computation of generalized least squares as a simple modification of J. A. Hausman's (1978) procedure for the homoskedastic case. Two methods for estimating the variance components are proposed. The first estimates the variance components based on ordinary least squares and within residuals. The second uses the minimum norm quadratic unbiased estimation procedure suggested by C. R. Rao (1970). These methods are then applied to the estimation of gasoline demand for eighteen OECD countries ove...
Our article presents a general treatment of the linear regression model, in which the error distribu...
peer reviewedThis paper proposes an extension of the standard one-way error components model allowin...
We consider the problem of estimating a partially linear panel data model whenthe error follows an o...
The purpose of this paper is to suggest an estimator which is more efficient than the within-class e...
The linear panel data estimator proposed by Hausman and Taylor relaxes the hypothesis of exogenous r...
The linear panel data estimator proposed by Hausman and Taylor relaxes the hypothesis of exogenous r...
The linear panel data estimator proposed by Hausman and Taylor relaxes the hypothesis of exogenous r...
A relevant issue in panel data estimation is heteroskedasticity, which often occurs when the sample ...
A relevant issue in panel data estimation is heteroskedasticity, which often occurs when the sample ...
We consider a linear model with normally distributed but heteroscedastic errors. When the error vari...
This paper constructs tests for heteroskedasticity in one-way error components models, in line with ...
In this paper, we study maximum likelihood estimation and Lagrange multiplier testing of a one-way e...
This paper studies instrumental variables (IV) estimation for an error component model with stationa...
This paper provides a simple estimation method for an error component regression model with general ...
Our article presents a general treatment of the linear regression model, in which the error distribu...
Our article presents a general treatment of the linear regression model, in which the error distribu...
peer reviewedThis paper proposes an extension of the standard one-way error components model allowin...
We consider the problem of estimating a partially linear panel data model whenthe error follows an o...
The purpose of this paper is to suggest an estimator which is more efficient than the within-class e...
The linear panel data estimator proposed by Hausman and Taylor relaxes the hypothesis of exogenous r...
The linear panel data estimator proposed by Hausman and Taylor relaxes the hypothesis of exogenous r...
The linear panel data estimator proposed by Hausman and Taylor relaxes the hypothesis of exogenous r...
A relevant issue in panel data estimation is heteroskedasticity, which often occurs when the sample ...
A relevant issue in panel data estimation is heteroskedasticity, which often occurs when the sample ...
We consider a linear model with normally distributed but heteroscedastic errors. When the error vari...
This paper constructs tests for heteroskedasticity in one-way error components models, in line with ...
In this paper, we study maximum likelihood estimation and Lagrange multiplier testing of a one-way e...
This paper studies instrumental variables (IV) estimation for an error component model with stationa...
This paper provides a simple estimation method for an error component regression model with general ...
Our article presents a general treatment of the linear regression model, in which the error distribu...
Our article presents a general treatment of the linear regression model, in which the error distribu...
peer reviewedThis paper proposes an extension of the standard one-way error components model allowin...
We consider the problem of estimating a partially linear panel data model whenthe error follows an o...