A simple computational method improves mean squared prediction error in small area estimation, suggesting enhanced modeling techniques.
The basic requirements of second-order unbiasedness and non-negativity of the mean squared prediction error (MSPE) of an empirical best predictor (EBP) have led to different complex analytical adjustments to the naive parametric bootstrap technique for small area estimation. In this paper, we show a way to recover the basic simplicity in the parametric bootstrap method, i.e. replacement of laborious analytical calculations by computer-oriented simple techniques, without sacrificing the basic requirements in an MSPE estimator. The method works for a general class of mixed models and different techniques of parameter estimation.
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Chatterjee et al. (2007) studied this question.
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