Cross-Sectional and Time Series Approaches to Small Area Estimation: Methods and Applications

University Of Georgia Research Foundation Inc, Athens GA

Investigators

Abstract

This research project will develop classical and Bayesian model-based statistical procedures with the primary goal of providing accurate estimates for small areas, representing local geographical regions and/or demographic subgroups of population. Primary motivation for this research stems from the need for precise estimates of small areas which facilitates the federal government's ability to initiate, formulate, and finally implement various socio-economic programs in order to address, among others, various public health issues, and income and poverty at local levels. Usually, most population surveys are designed to achieve a high level of efficiency at the global level which leads to direct survey based estimates of smaller areas having large standard errors. In many surveys (e.g., the Current Population Survey and the Current Employment Statistics Survey), data also are collected over time providing useful time series. This project will exploit the time series nature of data and produce reliable estimates of small areas by borrowing strength from information across small areas as well as those available over time. Explicit models will be developed incorporating linear as well as some recently developed nonlinear time series models. In this context, the study will develop suitable statistical methodologies such as empirical best linear unbiased prediction, empirical Bayes and hierarchical Bayes estimation methods to produce reliable small area point and interval estimates useful in various federal and local government programs. Markov chain Monte Carlo methods will be used in fitting Bayesian models. Software based on SAS, FORTRAN, S-plus, and SCA will be developed for implementing methodologies developed in this project. Furthermore, measure of accuracy of small area estimators will be assessed through second order approximations for the mean squared error of these estimators, even when the underlying distributions are unspecified, to check if the end results are robust against non-normality. The importance of this project lies in its relevance and direct tie to some of the on-going programs in the Census Bureau and the Bureau of Labor Statistics. This research will lead to precise estimates of (i) poverty rates and median family income in the Small Area Income and Poverty Estimates program launched by the Census Bureau, (ii) U.S. civilian unemployment rates, and (iii) accurate employment counts for major industries in various regions. This research will, on the one hand, advance statistical methodology for small area estimation and, on the other hand, apply various newly developed methodologies to statistical issues important to society.