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In this article we describe and apply the Fay-Herriot model with spatiallycorrelated random area effects (Pratesi, M., & Salvati, N. (2008)), in order topredict the prevalence of anemia and childhood stunting in Peruvian districts,based on the data from the Demographic and Family Health Survey of the year2019, which collects data about anemia and childhood stunting for childrenunder the age of 12 years, and the National Census carried out in 2017. Ourmain objective is to produce reliable predictions for the districts, wheresample sizes are too small to provide good direct estimates, and for thedistricts, which were not included in the sample. The basic Fay-Herriot model(Fay & Herriot, 1979) tackles this problem by incorporating auxiliaryinformation, which is generally available from administrative or censusrecords. The Fay-Herriot model with spatially correlated random area effects,in addition to auxiliary information, incorporates geographic information aboutthe areas, such as latitude and longitude. This permits modeling spatialautocorrelations, which are not unusual in socioeconomic and health surveys. Toevaluate the mean square error of the above-mentioned predictors, we use theparametric bootstrap procedure, developed in Molina et al. (2009).

apuntes

Is it possible to obtain reliable estimates for the prevalence of anemia  and childhood stunting among children under 5 in the poorest districts in  Peru?