Add to ORKGA relevant issue in panel data estimation is heteroskedasticity, which often occurs when the sample size is large and individual units are of varying size. Furthermore, many of the available panel data sets are unbalanced in nature, because of attrition or accretion, and micro-econometric models applied to panel data are frequently multi-equation models. This paper considers the general least squares estimation of heteroskedastic stratified two-way error component model of both single equations and seemingly unrelated regressions (SUR) systems (with cross-equations restrictions) on unbalanced panel data. The derived heteroskedastic estimators improve the estimation efficiency, with the SUR procedures performing better than the singleequatio...
Panel data sets, also called longitudinal data sets, are sets of data where the same units (for inst...
We consider the problem of estimating a partially linear panel data model whenthe error follows an o...
This paper investigates the efficiency of four methods of estimating panel data models (Pooling (OLS...
A relevant issue in panel data estimation is heteroskedasticity, which often occurs when the sample ...
A relevant issue in panel data estimation is heteroscedasticity, which often occurs when the sample ...
This paper considers the two-way error components model (ECM) estimation of seemingly unrelated regr...
This article considers the two-way error components model (ECM) estimation of seemingly unrelated re...
Fixed effects panel data regression models are useful tools in econometric and microarray analysis. ...
This article considers the two-way error components model (ECM) estimation of seemingly unrelated re...
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...
Abstract: The panel data models are becoming more common in relation to cross-section and time serie...
This paper generalizes the one-way error component model from the homoskedastic to the heteroskedast...
In this paper, we study maximum likelihood estimation and Lagrange multiplier testing of a one-way e...
Panel data sets, also called longitudinal data sets, are sets of data where the same units (for inst...
We consider the problem of estimating a partially linear panel data model whenthe error follows an o...
This paper investigates the efficiency of four methods of estimating panel data models (Pooling (OLS...
A relevant issue in panel data estimation is heteroskedasticity, which often occurs when the sample ...
A relevant issue in panel data estimation is heteroscedasticity, which often occurs when the sample ...
This paper considers the two-way error components model (ECM) estimation of seemingly unrelated regr...
This article considers the two-way error components model (ECM) estimation of seemingly unrelated re...
Fixed effects panel data regression models are useful tools in econometric and microarray analysis. ...
This article considers the two-way error components model (ECM) estimation of seemingly unrelated re...
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...
Abstract: The panel data models are becoming more common in relation to cross-section and time serie...
This paper generalizes the one-way error component model from the homoskedastic to the heteroskedast...
In this paper, we study maximum likelihood estimation and Lagrange multiplier testing of a one-way e...
Panel data sets, also called longitudinal data sets, are sets of data where the same units (for inst...
We consider the problem of estimating a partially linear panel data model whenthe error follows an o...
This paper investigates the efficiency of four methods of estimating panel data models (Pooling (OLS...