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• W2401056240 abstract "A 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 (GLS) estimation of heteroskedastic stratified two-way error component model (ECM) 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 single-equation procedures." @default.
• W2401056240 created "2016-06-24" @default.
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• W2401056240 date "2016-05-18" @default.
• W2401056240 modified "2023-09-27" @default.
• W2401056240 title "Heteroskedastic Stratified Two-way Error Component Models of Single Equations and Seemingly Unrelated Regressions Systems" @default.
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