Small Area Estimation of Household Economic Indicators under Unit-Level Generalized Additive Models for Location, Scale and Shape

Abstract

We propose a small area estimation model based on Generalized Additive Models for Location, Scale and Shape (SAE-GAMLSS) for the estimation of household economic indicators. SAE-GAMLSS relax the exponential family distributional assumption and allow each distributional parameter to depend on covariates. A bootstrap approach to estimate the MSE is proposed. The SAE-GAMLSS estimator shows a largely better performance than the well-known Empirical Best Linear Unbiased Predictor (EBLUP) under various simulated scenarios. Per-capita consumption of Italian and foreign households in Italian regions, in urban and rural areas, is estimated using SAE-GAMLSS. Results show that the well-known Italian North–South divide does not hold for foreigners.