Data integration and small domain estimation in Poland – experiences and  problems

Elżbieta Gołata

doi:https://doi.org/10.59170/stattrans-2012-008

Data integration and small domain estimation in Poland – experiences and problems

Elżbieta Gołata Poznan University of Economics, Department of Statistics, al. Niepodległości 10, 61-875 Poznań, Poland Statistics in Transition new series, vol. 13, 2012, 1, pages: 107-142 Published online: 1 March 2012 https://doi.org/10.59170/stattrans-2012-008

192 Views 14 Downloads

ARTICLE

(English) PDF

ABSTRACT

The aim of the study could be identified twofold. On the one hand, it was a presentation of Polish experiences as concerns the most important methodological issues of contemporary statistics. These are the problems of data integration (DI) and statistical estimation for small domains (SDE).On the other hand, attempts to determine relationship between these two groups of methods were undertaken. Given convergence of the objectives of both SDE and DI, that is: striving to increase efficiency of the use of existing sources of information, simulation study was conducted. It was aimed at verifying the hypothesis of synergies referring to combined application of both groups of methods: SDE and DI.

KEYWORDS

Small domain estimation, data integration

REFERENCES

BACHER, J. (2002) Statistisches Matching - Anwendungsmöglichkeiten, Verfahren und ihre praktische Umsetzung in SPSS, ZA-Informationen, 51. Jg.

BALIN, M., D’ORAZIO, M., DI ZIO, M., SCANU, M., TORELLI, N. (2009) Statistical Matching of Two Surveys with a Common Subset, Working Paper n. 124, Universita Degli Studi di Trieste, Dipartimento di Scienze Economiche e Statistiche.

BRACHA, C. (1994) Metodologiczne aspekty badania małych obszarów [Methodological Aspects of Small Area Studies], „Studia i Materiały. Z Prac Zakładu Badań Statystyczno-Ekonomicznych” nr 43, GUS, Warszawa (in Polish).

CHAMBERS, R., SAEI A. (2003) Linear Mixed Model with Spatial Correlated Area Effect in Small Area Estimation.

CHAMBERS, R., SAEI A., 2004, Small Area Estimation Under Linear and Generalized Linear Mixed Models With Time and Area Effects, Southampton Statistical Sciences Research Institute.

CHAMBERS, R.L, FALVEY, H., HEDLIN, D., KOKIC P. (2001) Does the Model Matter for GREG Estimation? A Business Survey Example, in: Journal of Official Statistics, Vol.17, No.4, 527-544.

CHAMBERS, R.L. (1996) Robust case-weighting for multipurpose establishment Surveys in: Journal of Official Statistics, Vol.12, No.1, 3-32.

CHOUDHRY, G.H., RAO, J.N.K. (1993) Evaluation of Small Area Estimators. An Empirical Study, in: Small Area Statistics and Survey Designs, eds G. Kalton, J. Kordos, R. Platek, vol. I: Invited Papers, Central Statistical Office, Warsaw.

D’ORAZIO, M., DI ZIO, M., SCANU, M. (2006) Statistical Matching. Theory and Practice, John Wiley & Sons, Ltd.

DEHNEL, G. (2010) Rozwój mikroprzedsiębiorczości w Polsce w świetle estymacji dla małych domen [Development of micro-business in the light of estimation for small domains], Wydawnictwo Uniwersytetu Ekonomicznego w Poznaniu, Poznan (in Polish).

DEHNEL, G. (2011) Use of Administrative Data for Business Statistics, Final Report under the grant agreement No. 30121.2009.004-2009.807, GUS, Warszawa.

DEVILLE, J–C. SÄRNDAL, C–E. (1992) Calibration Estimators in Survey Sampling, in Journal of the American Statistical Association, Vol. 87, 376–382.

DI ZIO, M. (2007) What is statistical matching, Course on Methods for Integration of Surveys and Administrative Data, Budapest, Hungary.

Eurarea Project Reference Volume All Parts (2004) The EURAREA Consortium http://www.ons.gov.uk/ons/guide-method/method-quality/general methodology/spatial-analysis-and-modelling/eurarea/downloads/index.html.

GHOSH, M., RAO J.N.K. (1994) Small Area Estimation: An Appraisal, „Statistical Science” vol. 9, no. 1.

GOŁATA, E. (2009) Opracowanie dla wybranych metod integracji danych reguł, procedur integracji danych z różnych źródeł, [Development of selected methods for data integration rules, procedures, data integration from various sources]. GUS Internal materials, Poznań, Poland (in Polish).

GOLATA, E. (2011) A study into the use of methods developed by small area statistics in: Use of Administrative Data for Business Statistics (pp.84-111), G. Dehnel (ed.), Final Report under the grant agreement No. 30121.2009.004-2009.807, GUS, Warszawa.

HEADY, P., HENNEL S. (2002) Small Area Estimation and the Ecological Effect – Modifying Standard Theory for Practical Situations, Office for National Statistics, London, IST 2000-26290 EURAREA, Enhancing Small Area Estimation Techniques to Meet European Needs.

HERZOG, T. N., SCHEUREN, F. J., WINKLER, W.E. (2007) Data Quality and Record Linkage Techniques, Springer New York.

KADANE, J.B. (2001) Some Statistical Problems in Merging Data Files, Journal of Official Statistics, No. 17, 423-433.

LEHTONEN, R., VEIJANEN, A. (1998) Logistics Generalized Regression Estimators, Survey Methodology, vol. 24.

MŁODAK, A., KUBACKI, J. (2010), A typology of Polish farms using some fuzzy classification method, Statistics in Transition – new series, vol. 11, No. 3, pp. 615 – 638.

MORIARITY, C., SCHEUREN, F. (2001) Statistical Matching: A Paradigm for Assessing the Uncertainty in the Procedure in: Journal of Official Statistics, No. 17, 407-422.

Multivariate analysis of systematic errors in the Census 2002, and statistical analysis of the variables of NC 2002 supporting the use of small area estimates. J. Paradysz (ed.), Report for Central Statistical Office, November 2008, Centre for Regional Statistics, University of Economics in Poznan (in Polish)

PARADYSZ, J. (2010), Konieczność estymacji pośredniej na użytek spisów powszechnych, [Necessity of indirect estimation in national census] in: Pomiar i informacja w gospodarce [Measurement and Information in the Economy] Gołata (ed.) published by Poznan University of Economics (in Polish).

PFEFFERMANN, D. (1999) Small Area Estimation – Big Developments, in: Small Area Estimation, International Association of Survey Statisticians Satellite Conference Proceedings, Riga 20-21 August 1999, Latvia.

PIETRZAK-RYNARZEWSKA, B., JOZEFOWSKI, T. (2010) Ocena możliwości wykorzystania rejestru PESEL w spisie ludności, [Assessment of the possibilities of using population register in the census] in: Pomiar i informacja w gospodarce [Measurement and Information in the Economy], Gołata (ed.) published by Poznan University of Economics (in Polish).

RAESSLER S. (2002) Statistical Matching. A Frequentist Theory, Practical Applications, and Alternative Bayesian Approaches, Springer, New York, USA.

RAO, J.N.K. (1999) Some Recent Advances in Model-Based Small Area Estimation in: Survey Methodology, vol. 25, Statistics Canada.

RAO, J.N.K. (2003) Small Area estimation, Wiley-Interscience.

RAO, J.N.K. (2005) Interplay Between Sample Survey Theory and Practice: An Appraisal, Survey Methodology, Vol. 31, No. 2, 117-138.

RENSSEN, R. H. (1998) Use of Statistical Matching Techniques in Calibration Estimation in: Survey Methodology, Vol. 24, No. 2, 171 – 183, Statistics Canada.

ROSZKA, W. (2011) An attempt to apply statistical data integration using data from sample surveys in: Economics, Management and Tourism, South-West University “Neofit Rilsky” Faculty of Economics and Tourism Department, Duni Royal Resort, Bulgaria.

RUBIN, D. B. (1986) Statistical Matching Using File Concatenation with Adjusted Weights and Multiple Imputations, in: Journal of Business and Economic Statistics, Vol. 4, No. 1, 87 – 94, stable URL: http://www.jstor.org/stable/1391390.

SäRNDAL, C.E., SWENSSON B., WRETMAN J. (1992) Model Assisted Survey Sampling, Springer Verlag, New York.

SÄRNDAL, C. E. (2007) The Calibration Approach in Survey Theory and Practice in: Survey Methodology. Vol. 33, No. 2, 99–119.

SÄRNDAL, C–E., LUNDSTRÖM S. (2005) Estimation in Surveys with Nonresponse, John Wiley & Sons, Ltd.

SCANU, M. (2010) Introduction to statistical matching in: ESSNet on Data Integration. Draft Report of WP1. State of the art on statistical methodologies for data integration, ESSNet.

SCHEUREN, F. (1989) A Comment on “The Social Policy Simulation Database and Model: An Example of Survey and Administrative Data Integration”, Survey of Current Business, 40-41.

SKINNER, C. (1991) The Use of Estimation Techniques to Produce Small Area Estimates, A report prepared for OPCS, University of Southampton.

SZYMKOWIAK, M. (2011) Assessing the feasibility of using information from administrative databases for calibration in short-term and annual business statistics in: Use of Administrative Data for Business Statistics (2011) Final Report under the grant agreement No. 30121.2009.004-2009.807, GUS, Warszawa.

Use of Administrative Data for Business Statistics (2011) G. Dehnel (ed.), Final Report under the grant agreement No. 30121.2009.004-2009.807, GUS, Warszawa.

VAN DER PUTTEN, P., KOK, J. N., GUPTA, A, (2002) Data Fusion through Statistical Matching, Center for eBusiness, MIT, USA.

VEIJANEN, A., DJERF, K., SŐSTRA, K., LEHTONEN, R., NISSINEN, K. (2004) EBLUPGREG.sas, program for small area estimation borrowing srength over time and space using unit level model, Statistics Finland, University of Jyväskylä.

WALLGREN, A., WALGREN, B. (2007) Registered based Statistics Administrative Data for Statistical Purposes, John Wiley & Sons Ltd.

WINKLER, W.E. (1990) String Comparator Metrics and Enhanced Decision Rules in the Fellegi-Sunter Model of Record Linkage, in: Section on Survey Research Methods, 354-359, American Statistical Association.

WINKLER, W.E. (1994) Advanced Methods For Record Linkage, Bureau of the Census, Washington DC 20233-9100.

WINKLER, W.E. (1995) Matching and Record Linkage, in: Business Survey Methods, B. Cox ed. 355-384, J. Wiley, New York.

WINKLER, W.E. (1999) The State of Record Linkage and Current Research Problems, RR99-04, U.S. Bureau of the Census, http://www.census.gov/srd/www/byyear.html.

WINKLER, W.E. (2001) Quality of Very Large Databases, RR2001/04, U.S. Bureau of the Census.

WU, CH. (2005) Algorithms and R Codes for the Pseudo Empirical Likelihood Method in Survey Sampling in: Survey Methodology, Vol. 31, No. 2, 239