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

Azevedo Neves André Felipe (), Nascimento Silva Denise Britz Do () and Silva Moura Fernando Antônio Da ()
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Azevedo Neves André Felipe: National School of Statistical Sciences, ; Rio de Janeiro, ; Brazil .
Nascimento Silva Denise Britz Do: National School of Statistical Sciences, ; Rio de Janeiro, ; Brazil .
Silva Moura Fernando Antônio Da: Statistics Department of Federal University of Rio de Janeiro, ; Rio de Janeiro, ; Brazil .

Statistics in Transition New Series, 2020, vol. 21, issue 4, 84-102

Abstract: 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.

Keywords: Annual Service Sector Survey; hierarchical Bayesian model. (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:stintr:v:21:y:2020:i:4:p:84-102:n:7

DOI: 10.21307/stattrans-2020-032

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