Small Area Predictors with Dual Shrinkage of Means and Variances

Hiromasa Tamae and Tatsuya Kubokawa
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Hiromasa Tamae: Graduate School of Economics, The University of Tokyo
Tatsuya Kubokawa: Faculty of Economics, The University of Tokyo

No CIRJE-F-982, CIRJE F-Series from CIRJE, Faculty of Economics, University of Tokyo

Abstract: The paper concerns small-area estimation in the Fay-Herriot type area-level model with random dispersions, which models the case that the sampling errors change from area to area. The resulting Bayes estimator shrinks both means and variances, but needs numerical computation to provide the estimates. In this paper, an approximated empirical Bayes (AEB) estimator with a closed form is suggested. The model parameters are estimated via the moment method, and the mean squared error of the AEB is estimated via the single parametric bootstrap method. The benchmarked estimator and a second-order unbiased estimator of the mean squared error are also derived. --

Pages: 25 pages
Date: 2015-07
New Economics Papers: this item is included in nep-ecm
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