Heteroscedastic Nested Error Regression Models with Variance Functions

Shonosuke Sugasawa and Tatsuya Kubokawa
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Shonosuke Sugasawa: Graduate School of Economics, The University of Tokyo
Tatsuya Kubokawa: Faculty of Economics, The University of Tokyo

No CIRJE-F-978, CIRJE F-Series from CIRJE, Faculty of Economics, University of Tokyo

Abstract: The article considers a nested error regression model with heteroscedastic variance functions for analyzing clustered data, where the normality for the underlying distributions is not assumed. Classical methods in normal nested error regression models with homogenous variances are extended in the two directions: heterogeneous variance functions for error terms and non-normal distributions for random effects and error terms. Consistent estimators for model parameters are suggested, and second-order approximations of their biases and variances are derived. The mean squared errors of the empirical best linear unbiased predictors are expressed explicitly to second-order. Second-order unbiased estimators of the mean squared errors are provided analytically in closed forms. The proposed model and the resulting procedures are numerically investigated through simulation and empirical studies. --

Pages: 28pages
Date: 2015-06
New Economics Papers: this item is included in nep-ecm
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