An empirical evaluation of small area estimators
This paper investigates the comparative performance of five small area estimators. We use Monte Carlo simulation in the context of both theoretical and empirical 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 square bias, and another one that uses area specific estimates of variance and square bias. It is found that among the feasible estimators, the best choice is the one that uses area specific estimates of variance and square bias.
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
- 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.
- 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.
When requesting a correction, please mention this item's handle: RePEc:upf:upfgen:674. See general information about how to correct material in RePEc.
If references are entirely missing, you can add them using this form.