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Heteroscedastic Nested Error Regression Models with Variance Functions

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  • Shonosuke Sugasawa

    (Graduate School of Economics, The University of Tokyo)

  • Tatsuya Kubokawa

    (Faculty of Economics, The 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. --

Suggested Citation

  • Shonosuke Sugasawa & Tatsuya Kubokawa, 2015. "Heteroscedastic Nested Error Regression Models with Variance Functions," CIRJE F-Series CIRJE-F-978, CIRJE, Faculty of Economics, University of Tokyo.
    • Handle: RePEc:tky:fseres:2015cf978
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    References listed on IDEAS

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    1. Gauri Sankar Datta & J. N. K. Rao & David Daniel Smith, 2005. "On measuring the variability of small area estimators under a basic area level model," Biometrika, Biometrika Trust, vol. 92(1), pages 183-196, March.
    2. Tapabrata Maiti & Hao Ren & Samiran Sinha, 2014. "Prediction Error of Small Area Predictors Shrinking Both Means and Variances," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(3), pages 775-790, September.
    3. Sharon L. Lohr & J. N. K. Rao, 2009. "Jackknife estimation of mean squared error of small area predictors in nonlinear mixed models," Biometrika, Biometrika Trust, vol. 96(2), pages 457-468.
    4. Ruppert, D. & Wand, M.P. & Holst, U. & Hossjer, O., "undated". "Local Polynomial Variance Function Estimation," Statistics Working Paper _007, Australian Graduate School of Management.
    5. Tatsuya Kubokawa & Shonosuke Sugasawa & Malay Ghosh & Sanjay Chaudhuri, 2014. "Prediction in Heteroscedastic Nested Error Regression Models with Random Dispersions," CIRJE F-Series CIRJE-F-939, CIRJE, Faculty of Economics, University of Tokyo.
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    Cited by:

    1. Sugasawa, Shonosuke & Kubokawa, Tatsuya, 2017. "Bayesian estimators in uncertain nested error regression models," Journal of Multivariate Analysis, Elsevier, vol. 153(C), pages 52-63.

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