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Estimation of small area counts with the benchmarking property

Author

Listed:
  • M. Giovanna Ranalli

    (Universitá degli Studi di Perugia)

  • Giorgio E. Montanari

    (Universitá degli Studi di Perugia)

  • Cecilia Vicarelli

    (Direzione Centrale per le Statistiche Congiunturali, ISTAT
    Direzione Centrale per la Contabilita’ Nazionale)

Abstract

Estimation of small area totals makes use of auxiliary variables to borrow strength from related areas through a model. Precision of final small area estimates depends on the validity of such a model. To protect against possible model failures, benchmarking procedures make the sum of small area estimates match a design consistent estimate of the total of a larger area. This is also particularly important for national institutes of statistics to ensure coherence between small area estimates and direct estimates produced at higher level planned domains. In this paper we propose a self-benchmarked estimator of small area totals which is based on a unit level logistic mixed model for a binary response. In particular, we use a plug-in approach and add a constraint to the maximum penalized quasi-likelihood (PQL) procedure considered in Saei and Chambers (Working paper M03/15, Southampton Statistical Sciences Research Institute. University of Southampton, 2003) to accommodate benchmarking. An analytic estimator for the mean squared error of the final small area estimator is also proposed following the ad-hoc procedure proposed by González-Manteiga et al. (Comput Stat Data Anal 51:2720–2733, 2007). We then compare the performance of the proposed benchmarked estimator with several competing estimators through a set of simulation studies.

Suggested Citation

  • M. Giovanna Ranalli & Giorgio E. Montanari & Cecilia Vicarelli, 2018. "Estimation of small area counts with the benchmarking property," METRON, Springer;Sapienza Università di Roma, vol. 76(3), pages 349-378, December.
  • Handle: RePEc:spr:metron:v:76:y:2018:i:3:d:10.1007_s40300-018-0146-2
    DOI: 10.1007/s40300-018-0146-2
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    References listed on IDEAS

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    1. Sophia Rabe‐Hesketh & Anders Skrondal, 2006. "Multilevel modelling of complex survey data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(4), pages 805-827, October.
    2. Isabel Molina & Ayoub Saei & M. José Lombardía, 2007. "Small area estimates of labour force participation under a multinomial logit mixed model," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(4), pages 975-1000, October.
    3. G. Datta & M. Ghosh & R. Steorts & J. Maples, 2011. "Bayesian benchmarking with applications 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. 20(3), pages 574-588, November.
    4. 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.
    5. Malay Ghosh & Rebecca Steorts, 2013. "Two-stage benchmarking as 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. 22(4), pages 670-687, November.
    6. Gonzalez-Manteiga, W. & Lombardia, M.J. & Molina, I. & Morales, D. & Santamaria, L., 2007. "Estimation of the mean squared error of predictors of small area linear parameters under a logistic mixed model," Computational Statistics & Data Analysis, Elsevier, vol. 51(5), pages 2720-2733, February.
    7. Edward L. Korn & Barry I. Graubard, 2003. "Estimating variance components by using survey data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(1), pages 175-190, February.
    8. 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.
    9. 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.
    10. Pfeffermann, Danny & Barnard, Charles H, 1991. "Some New Estimators for Small-Area Means with Application to the Assessment of Farmland Values," Journal of Business & Economic Statistics, American Statistical Association, vol. 9(1), pages 73-84, January.
    11. Jiming Jiang & P. Lahiri, 2006. "Mixed model prediction and small area estimation," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 15(1), pages 1-96, June.
    12. M. Ugarte & A. Militino & T. Goicoa, 2009. "Benchmarked estimates in small areas using linear mixed models with restrictions," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 18(2), pages 342-364, August.
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