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Small area models for skewed Brazilian business survey data

Author

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  • Fernando A. S. Moura
  • André Felipe Neves
  • Denise Britz do N. Silva

Abstract

The Brazilian Institute of Geography and Statistics performs an annual service survey that focuses on segments of the tertiary sector. Sample estimates for some economic activities in the north, north‐east and midwest regions of Brazil have low precision due to the sample design. Furthermore, one of the main variables of interest is considerably skewed with potential outliers. To overcome this problem, skew normal and skew t‐models are proposed to produce model‐based estimates. The small domain estimation models relate operating revenue variables to potential auxiliary variables (the number of employed people and wages) obtained from a business register. The models proposed are compared with the usual Fay–Herriot model under the assumptions of known and unknown sampling variances and its transformed log‐version under the assumption of known variances. The evaluation studies with real business survey data show that the models proposed seem to be more efficient for small area predictions under skewed data than the customarily employed Fay–Herriot model, as well as its log‐normal version.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:jorssa:v:180:y:2017:i:4:p:1039-1055
    DOI: 10.1111/rssa.12301
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. André Felipe Azevedo Neves & Denise Britz do Nascimento Silva & Fernando Antônio da Silva Moura, 2020. "Skew normal small area time models for the Brazilian annual service sector survey," Statistics in Transition New Series, Polish Statistical Association, vol. 21(4), pages 84-102, August.
    3. Jan Pablo Burgard & Domingo Morales & Anna-Lena Wölwer, 2022. "Small area estimation of socioeconomic indicators for sampled and unsampled domains," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 106(2), pages 287-314, June.
    4. Katarzyna Reluga & María‐José Lombardía & Stefan Sperlich, 2023. "Simultaneous inference for linear mixed model parameters with an application to small area estimation," International Statistical Review, International Statistical Institute, vol. 91(2), pages 193-217, August.
    5. Kreutzmann, Ann-Kristin & Marek, Philipp & Salvati, Nicola & Schmid, Timo, 2019. "Estimating regional wealth in Germany: How different are East and West really?," Discussion Papers 35/2019, Deutsche Bundesbank.

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