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Small area estimation of poverty proportions under unit-level temporal binomial-logit mixed models

Author

Listed:
  • Tomáš Hobza

    (Czech Technical University in Prague)

  • Domingo Morales

    (Miguel Hernández University of Elche)

  • Laureano Santamaría

    (Miguel Hernández University of Elche)

Abstract

Poverty proportions are averages of dichotomic variables that can be explained by unit-level binomial-logit mixed models. The change between the poverty proportions of two consecutive years is an indicator describing the evolution of poverty. This paper applies a unit-level temporal binomial-logit mixed model for estimating poverty proportions and their changes. The model parameters are estimated by the maximum likelihood method for the Laplace approximation of the loglikelihood. The empirical best predictors (EBP) of proportions and changes are calculated and compared with plug-in estimators. The mean squared error of the EBP is estimated by a parametric bootstrap. A simulation experiment is carried out to study the empirical behavior of the EBP and the plug-in estimators. An application to the estimation of poverty proportions and changes in counties of the region of Valencia, Spain, is given.

Suggested Citation

  • Tomáš Hobza & Domingo Morales & Laureano Santamaría, 2018. "Small area estimation of poverty proportions under unit-level temporal binomial-logit mixed models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(2), pages 270-294, June.
    • Handle: RePEc:spr:testjl:v:27:y:2018:i:2:d:10.1007_s11749-017-0545-3
    DOI: 10.1007/s11749-017-0545-3
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    References listed on IDEAS

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    2. Marhuenda, Yolanda & Molina, Isabel & Morales, Domingo, 2013. "Small area estimation with spatio-temporal Fay–Herriot models," Computational Statistics & Data Analysis, Elsevier, vol. 58(C), pages 308-325.
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    4. Jiming Jiang & P. Lahiri, 2001. "Empirical Best Prediction for Small Area Inference with Binary Data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 53(2), pages 217-243, June.
    5. Chris Elbers & Jean O. Lanjouw & Peter Lanjouw, 2003. "Micro--Level Estimation of Poverty and Inequality," Econometrica, Econometric Society, vol. 71(1), pages 355-364, January.
    6. D. Pfeffermann & S. Correa, 2012. "Empirical bootstrap bias correction and estimation of prediction mean square error in small area estimation," Biometrika, Biometrika Trust, vol. 99(2), pages 457-472.
    7. A. F. Militino & M. D. Ugarte & T. Goicoa, 2015. "Deriving small area estimates from information technology business surveys," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(4), pages 1051-1067, October.
    8. Miguel Boubeta & María José Lombardía & Domingo Morales, 2016. "Empirical best prediction under area-level Poisson mixed models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(3), pages 548-569, September.
    9. Boubeta, Miguel & Lombardía, María José & Morales, Domingo, 2017. "Poisson mixed models for studying the poverty in small areas," Computational Statistics & Data Analysis, Elsevier, vol. 107(C), pages 32-47.
    10. Esther López-Vizcaíno & María José Lombardía & Domingo Morales, 2015. "Small area estimation of labour force indicators under a multinomial model with correlated time and area effects," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(3), pages 535-565, June.
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    Cited by:

    1. Jan Pablo Burgard & María Dolores Esteban & Domingo Morales & Agustín Pérez, 2020. "A Fay–Herriot model when auxiliary variables are measured with error," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(1), pages 166-195, March.
    2. Żądło Tomasz, 2020. "On Accuracy Estimation Using Parametric Bootstrap in small Area Prediction Problems," Journal of Official Statistics, Sciendo, vol. 36(2), pages 435-458, June.
    3. Roberto Benavent & Domingo Morales, 2021. "Small area estimation under a temporal bivariate area-level linear mixed model with independent time effects," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(1), pages 195-222, March.
    4. Angelo Moretti, 2023. "Estimation of small area proportions under a bivariate logistic mixed model," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(4), pages 3663-3684, August.
    5. Isabel Molina & Paul Corral & Minh Nguyen, 2022. "Estimation of poverty and inequality in small areas: review and discussion," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(4), pages 1143-1166, December.
    6. María Dolores Esteban & María José Lombardía & Esther López-Vizcaíno & Domingo Morales & Agustín Pérez, 2020. "Small area estimation of proportions under area-level compositional mixed models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(3), pages 793-818, September.
    7. Żądło Tomasz, 2020. "On Accuracy Estimation Using Parametric Bootstrap in small Area Prediction Problems," Journal of Official Statistics, Sciendo, vol. 36(2), pages 435-458, June.
    8. Monique Graf & J. Miguel Marín & Isabel Molina, 2019. "A generalized mixed model for skewed distributions applied to small area estimation," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(2), pages 565-597, June.
    9. Hao Sun & Emily Berg & Zhengyuan Zhu, 2022. "Bivariate small‐area estimation for binary and gaussian variables based on a conditionally specified model," Biometrics, The International Biometric Society, vol. 78(4), pages 1555-1565, December.
    10. Domingo Morales & Joscha Krause & Jan Pablo Burgard, 2022. "On the Use of Aggregate Survey Data for Estimating Regional Major Depressive Disorder Prevalence," Psychometrika, Springer;The Psychometric Society, vol. 87(1), pages 344-368, March.
    11. Joscha Krause & Jan Pablo Burgard & Domingo Morales, 2022. "Robust prediction of domain compositions from uncertain data using isometric logratio transformations in a penalized multivariate Fay–Herriot model," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 76(1), pages 65-96, February.
    12. María Dolores Esteban & María José Lombardía & Esther López-Vizcaíno & Domingo Morales & Agustín Pérez, 2023. "Small area estimation of average compositions under multivariate nested error regression models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(2), pages 651-676, June.
    13. Joscha Krause & Jan Pablo Burgard & Domingo Morales, 2022. "$$\ell _2$$ ℓ 2 -penalized approximate likelihood inference in logit mixed models for regional prevalence estimation under covariate rank-deficiency," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 85(4), pages 459-489, May.
    14. Fúquene-Patiño Jairo & Cristancho César & Ospina Mariana & Gonzalez Domingo Morales, 2021. "Fay-Herriot Model-Based Prediction Alternatives for Estimating Households with Emigrated Members," Journal of Official Statistics, Sciendo, vol. 37(3), pages 771-789, September.
    15. Kevin Dayaratna & Jesse Crosson & Chandler Hubbard, 2022. "Closed Form Bayesian Inferences for Binary Logistic Regression with Applications to American Voter Turnout," Stats, MDPI, vol. 5(4), pages 1-21, November.
    16. Guadarrama, María & Morales, Domingo & Molina, Isabel, 2021. "Time stable empirical best predictors under a unit-level model," Computational Statistics & Data Analysis, Elsevier, vol. 160(C).

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