An Empirical Evaluation of Five Small Area Estimators
This paper compares five small area estimators. We use Monte Carlo simulation in the context of both artificial and real populations. In addition to the direct and indirect estimators, we consider the optimal composite estimator with population weights, and two composite estimators with estimated weights: one that assumes homogeneity of within area variance and squared bias and one that uses area-specific estimates of variance and squared bias. In the study with real population, we found that among the feasible estimators, the best choice is the one that uses area-specific estimates of variance and squared bias.
|Date of creation:||15 Dec 2003|
|Date of revision:|
|Note:||Type of Document - pdf; prepared on Win2000; to print on Hewlett Packard Laserjet; pages: 23; figures: 7|
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- Farrell, Patrick J & MacGibbon, Brenda & Tomberlin, Thomas J, 1997. "Empirical Bayes Small-Area Estimation Using Logistic Regression Models and Summary Statistics," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(1), pages 101-8, January.
- Isaki, Cary T, 1990. "Small-Area Estimation of Economic Statistics," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(4), pages 435-41, October.
- Pfeffermann, Danny & Barnard, Charles H, 1991. "Some New Estimators for Small-Area Means with Application to the Assessment of Farmland Values," Journal of Business & Economic Statistics, American Statistical Association, vol. 9(1), pages 73-84, January.
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