Skew normal small area time models for the Brazilian annual service sector survey

Small domain estimation covers a set of statistical methods for estimating quantities in domains not previously considered by the sample design. In such cases, the use of a model-based approach that relates sample estimates to auxiliary variables is indicated. In this paper, we propose and evaluate skew normal small area time models for the Brazilian Annual Service Sector Survey (BASSS), carried out by the Brazilian Institute of Geography and Statistics (IBGE). The BASSS sampling plan cannot produce estimates with acceptable precision for service activities in the North, Northeast and Midwest regions of the country. Therefore, the use of small area estimation models may provide acceptable precise estimates, especially if they take into account temporal dynamics and sector similarity. Besides, skew normal models can handle business data with asymmetric distribution and the presence of outliers. We propose models with domain and time random effects on the intercept and slope. The results, based on 10-year survey data (2007-2016), show substantial improvement in the precision of the estimates, albeit with presence of some bias.