Poverty mapping under a two-fold Fay-Herriot model with random regression coefficients

N. Diz Rosales, M. J. Lombardía Cortiña, D. Morales

Under a two-fold Fay-Herriot model with random intercepts and random regression coefficients, we derive area-level predictors for poverty proportions and introduce analytical and bootstrap-based estimators of the mean squared error. Residual maximum likelihood estimators of model parameters and mode predictors of random effects are calculated. Simulation studies are conducted to evaluate the performance of the estimation algorithm, predictors and bootstrap-based and analytical mean squared error estimators. The proposed statistical methodology is applied to data from the Spanish Living Conditions Survey 2022 with the objective of estimating poverty proportions by province, disaggregated by gender and age group. This work provides a rigorous and novel framework for estimation in small areas, providing a detailed mapping of the poverty ratio with precision and reliable measures of uncertainty.

Keywords: Mixed models, Fay-Herriot models, Poverty proportion, Random regression coefficients, Small area estimation

Mixt Models
June 13, 2025  11:00 AM
Auditorio 2. Leandre Cristòfol

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