IDEAS home Printed from https://ideas.repec.org/a/bla/jorssa/v180y2017i4p1039-1055.html
   My bibliography  Save this article

Small area models for skewed Brazilian business survey data

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/rssa.12301
    Download Restriction: no
    File URL: https://libkey.io/10.1111/rssa.12301?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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. Ray Chambers & Nikos Tzavidis, 2006. "M-quantile models for small area estimation," Biometrika, Biometrika Trust, vol. 93(2), pages 255-268, June.
    3. 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.
    4. Ray Chambers & Hukum Chandra & Nicola Salvati & Nikos Tzavidis, 2014. "Outlier robust small area estimation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 76(1), pages 47-69, January.
    5. 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.
    6. Datta, G. S. & Lahiri, P., 1995. "Robust Hierarchical Bayes Estimation of Small Area Characteristics in the Presence of Covariates and Outliers," Journal of Multivariate Analysis, Elsevier, vol. 54(2), pages 310-328, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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, Polish Statistical Association, 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Fabrizi, Enrico & Salvati, Nicola & Trivisano, Carlo, 2020. "Robust Bayesian small area estimation based on quantile regression," Computational Statistics & Data Analysis, Elsevier, vol. 145(C).
    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. 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, Polish Statistical Association, vol. 21(4), pages 84-102, August.
    4. Otto-Sobotka, Fabian & Salvati, Nicola & Ranalli, Maria Giovanna & Kneib, Thomas, 2019. "Adaptive semiparametric M-quantile regression," Econometrics and Statistics, Elsevier, vol. 11(C), pages 116-129.
    5. J. N. K. Rao, 2015. "Inferential issues in model-based small area estimation: some new developments," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 16(4), pages 491-510, December.
    6. G. Bertarelli & R. Chambers & N. Salvati, 2021. "Outlier robust small domain estimation via bias correction and robust bootstrapping," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(1), pages 331-357, March.
    7. Valéry Dongmo Jiongo & Pierre Nguimkeu, 2018. "Bootstrapping Mean Squared Errors of Robust Small-Area Estimators: Application to the Method-of-Payments Data," Staff Working Papers 18-28, Bank of Canada.
    8. Stefano Marchetti & Maciej Beręsewicz & Nicola Salvati & Marcin Szymkowiak & Łukasz Wawrowski, 2018. "The use of a three‐level M‐quantile model to map poverty at local administrative unit 1 in Poland," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(4), pages 1077-1104, October.
    9. Bhuiyan, M. Kamruj Jaman & Hossain, M. Jamal & Islam, Mohammad Amirul & Imam, M. Farouq & Quddus, Md. Abdul, 2020. "Small Area Estimation Of Nutritional Status Of Under-Five Children In Sylhet Division: An M-Quantile Approach," Bangladesh Journal of Agricultural Economics, Bangladesh Agricultural University, vol. 41(1), July.
    10. Merlo, Luca & Petrella, Lea & Salvati, Nicola & Tzavidis, Nikos, 2022. "Marginal M-quantile regression for multivariate dependent data," Computational Statistics & Data Analysis, Elsevier, vol. 173(C).
    11. Baldermann, Claudia & Salvati, Nicola & Schmid, Timo, 2016. "Robust small area estimation under spatial non-stationarity," Discussion Papers 2016/5, Free University Berlin, School of Business & Economics.
    12. Enrico Fabrizi & Maria Ferrante & Carlo Trivisano, 2013. "Small area estimation of labor productivity for the Italian manufacturing SME cross-classified by region, industry and size," ERSA conference papers ersa13p894, European Regional Science Association.
    13. Forough Karlberg, 2015. "Small Area Estimation For Skewed Data In The Presence Of Zeroes," Statistics in Transition New Series, Polish Statistical Association, vol. 16(4), pages 541-562, December.
    14. Chakraborty Adrijo & Datta Gauri Sankar & Mandal Abhyuday, 2016. "A Two-Component Normal Mixture Alternative to the Fay-Herriot Model," Statistics in Transition New Series, Polish Statistical Association, vol. 17(1), pages 67-90, March.
    15. N. Salvati & E. Fabrizi & M. G. Ranalli & R. L. Chambers, 2021. "Small area estimation with linked data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(1), pages 78-107, February.
    16. Dehnel Grażyna & Wawrowski Łukasz, 2020. "Robust estimation of wages in small enterprises: the application to Poland’s districts," Statistics in Transition New Series, Polish Statistical Association, vol. 21(1), pages 137-157, March.
    17. Paul A. Smith & Chiara Bocci & Nikos Tzavidis & Sabine Krieg & Marc J. E. Smeets, 2021. "Robust estimation for small domains in business surveys," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(2), pages 312-334, March.
    18. Ranjbar, Setareh & Salvati, Nicola & Pacini, Barbara, 2023. "Estimating heterogeneous causal effects in observational studies using small area predictors," Computational Statistics & Data Analysis, Elsevier, vol. 184(C).
    19. Stefano Marchetti & Caterina Giusti & Nicola Salvati & Monica Pratesi, 2017. "Small area estimation based on M-quantile models in presence of outliers in auxiliary variables," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 26(4), pages 531-555, November.
    20. J. N. K. Rao, 2015. "Inferential Issues In Model-Based Small Area Estimation: Some New Developments," Statistics in Transition New Series, Polish Statistical Association, vol. 16(4), pages 491-510, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:jorssa:v:180:y:2017:i:4:p:1039-1055. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.