Empirical best prediction of poverty indicators via nested error regression with high dimensional parameters

TL;DR

This paper extends the nested error regression model to high-dimensional parameters for small area poverty estimation, introducing efficient algorithms, out-of-sample predictions, and uncertainty quantification, demonstrating improved accuracy on real survey data.

Contribution

It develops a robust framework for empirical best predictors in high-dimensional settings, addressing computational challenges and enhancing out-of-sample area predictions.

Findings

Lower bias and prediction error compared to existing methods

Efficient estimation procedure reduces computation time

Improved out-of-sample poverty estimates for Albanian municipalities

Abstract

The Nested Error Regression Model with High-Dimensional Parameters (NERHDP) is extended to address challenges in small area poverty estimation. A robust and flexible framework is proposed to derive empirical best predictors (EBPs) of small area poverty indicators while accommodating heterogeneity in regression coefficients and sampling variances across areas. To mitigate the computational limitations of the existing algorithm, an efficient estimation procedure is introduced, substantially reducing computation time and enhancing scalability for large datasets. A novel approach for generating area-specific poverty estimates in out-of-sample areas is also developed, improving the reliability of synthetic estimates. Uncertainty is quantified through a parametric bootstrap method specifically tailored to the extended model. Under heterogeneous data-generating scenarios, the proposed method yields lower relative bias and relative root mean squared prediction error than existing approaches. The methodology is further illustrated using data from the 2002 Albania Living Standards Measurement Survey, combined with auxiliary information from the 2001 census, to estimate poverty indicators for 374 municipalities.