Bootstrap for estimating the mean squared error of the Spatial Eblup

This work assumes that the small area quantities of interest follow a Fay-Herriot model with spatially correlated random area effects. Under this model, parametric and non- parametric bootstrap procedures are proposed for estimating the mean squared error of the Empirical Best Linear Unbiased Predictor (EBLUP). A simulation study based on the Ital- ian Agriculture Census 2000 compares bootstrap and analytical estimates of the MSE and studies their robustness to non-normality. Results indicate lower bias for the non-parametric bootstrap under specific departures from normality.