Mixed Effects Prediction under Benchmarking and Applications to Small Area Estimation

Tatsuya Kubokawa
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Tatsuya Kubokawa: Faculty of Economics, University of Tokyo

No CIRJE-F-832, CIRJE F-Series from CIRJE, Faculty of Economics, University of Tokyo

Abstract: The empirical best linear unbiased predictor (EBLUP) in the linear mixed model (LMM) is useful for the small area estimation in the sense of increasing the precision of estimation of small area means. However, one potential difficulty of EBLUP is that when aggregated, the overall estimate for a larger geographical area may be quite different from the corresponding direct estimate like the overall sample mean. One way to solve this problem is the benchmarking approach, and the constrained EBLUP is a feasible solution which satisfies the constraints that the aggregated mean and variance are identical to the requested values of mean and variance. An interesting query is whether the constrained EBLUP may have a larger estimation error than EBLUP. In this paper, we address this issue by deriving asymptotic approximations of MSE of the constrained EBLUP. Also, we provide asymptotic unbiased estimators of the MSE of the constrained EBLUP based on the parametric bootstrap method, and establish their second-order justification. Finally, the performances of the suggested MSE estimators are numerically investigated.

Pages: 21 pages
Date: 2012-01
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:tky:fseres:2012cf832

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