Abstract

Authors: Schirripa Spagnolo, F., Salvati, N., Fabrizi, E.

This contribution proposes a new small area estimation approach that integrates Partial Least Squares within the Fay--Herriot model to address the challenges posed by high-dimensional sets of highly correlated auxiliary variables. The resulting Partial Fay--Herriot (PFH) estimator constructs supervised components that maximize their association with the target variable, enhancing model stability and predictive efficiency. Monte Carlo simulations demonstrate that the PFH estimator achieves lower mean squared error than the standard Fay--Herriot estimator and outperforms principal components–based alternatives while relying on fewer latent dimensions. The methodology is applied to the estimation of district-level per capita consumption in Mozambique, where the survey data source is complemented by a large set of correlated census variables. The resulting estimates highlight pronounced geographic heterogeneity and uncover spatial clusters of deprivation. Overall, the findings show that the proposed supervised dimension-reduction approach represents an effective and easily interpretable tool for producing reliable indicators in high-dimensional contexts.