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

Author

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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 .)

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.

Suggested Citation

  • Azevedo Neves André Felipe & Nascimento Silva Denise Britz do & Silva Moura Fernando Antônio da, 2020. "Skew normal small area time models for the Brazilian annual service sector survey," Statistics in Transition New Series, Statistics Poland, vol. 21(4), pages 84-102, August.
    • Handle: RePEc:vrs:stintr:v:21:y:2020:i:4:p:84-102:n:7
    DOI: 10.21307/stattrans-2020-032
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    References listed on IDEAS

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    1. Ferraz, V.R.S. & Moura, F.A.S., 2012. "Small area estimation using skew normal models," Computational Statistics & Data Analysis, Elsevier, vol. 56(10), pages 2864-2874.
    2. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    3. R. A. Sugden & T. M. F. Smith & R. P. Jones, 2000. "Cochran's rule for simple random sampling," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(4), pages 787-793.
    4. Fernando A. S. Moura & André Felipe Neves & Denise Britz do N. Silva, 2017. "Small area models for skewed Brazilian business survey data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(4), pages 1039-1055, October.
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