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Statistical challenges of administrative and transaction data

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  • David J. Hand

Abstract

Administrative data are becoming increasingly important. They are typically the side effect of some operational exercise and are often seen as having significant advantages over alternative sources of data. Although it is true that such data have merits, statisticians should approach the analysis of such data with the same cautious and critical eye as they approach the analysis of data from any other source. The paper identifies some statistical challenges, with the aim of stimulating debate about and improving the analysis of administrative data, and encouraging methodology researchers to explore some of the important statistical problems which arise with such data.

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  • David J. Hand, 2018. "Statistical challenges of administrative and transaction data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(3), pages 555-605, June.
    • Handle: RePEc:bla:jorssa:v:181:y:2018:i:3:p:555-605
    DOI: 10.1111/rssa.12315
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    1. Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2008. "Nowcasting: The real-time informational content of macroeconomic data," Journal of Monetary Economics, Elsevier, vol. 55(4), pages 665-676, May.
    2. James J. Heckman, 1976. "The Common Structure of Statistical Models of Truncation, Sample Selection and Limited Dependent Variables and a Simple Estimator for Such Models," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 5, number 4, pages 475-492, National Bureau of Economic Research, Inc.
    3. John P A Ioannidis, 2005. "Why Most Published Research Findings Are False," PLOS Medicine, Public Library of Science, vol. 2(8), pages 1-1, August.
    4. Christopher Berka & Stefan Humer & Mathias Moser & Manuela Lenk & Henrik Rechta & Eliane Schwerer, 2012. "Combination of evidence from multiple administrative data sources: quality assessment of the Austrian register‐based Census 2011," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 66(1), pages 18-33, February.
    5. Reiter, Jerome P., 2005. "Estimating Risks of Identification Disclosure in Microdata," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 1103-1112, December.
    6. Angela Luna & Li-Chun Zhang & Alison Whitworth & Kirsten Piller, 2015. "Small Area Estimates Of The Population Distribution By Ethnic Group In England: A Proposal Using Structure Preserving Estimators," Statistics in Transition New Series, Polish Statistical Association, vol. 16(4), pages 585-602, December.
    7. Claudio Agostinelli & Andy Leung & Victor Yohai & Ruben Zamar, 2015. "Rejoinder on: Robust estimation of multivariate location and scatter in the presence of cellwise and casewise contamination," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 24(3), pages 484-488, September.
    8. P. Lahiri & Michael D. Larsen, 2005. "Regression Analysis With Linked Data," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 222-230, March.
    9. Andrew Harvey & Chia‐Hui Chung, 2000. "Estimating the underlying change in unemployment in the UK," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 163(3), pages 303-309.
    10. Jelke Bethlehem, 2010. "Selection Bias in Web Surveys," International Statistical Review, International Statistical Institute, vol. 78(2), pages 161-188, August.
    11. Ross Meader & Geoff Tily, 2008. "Monitoring the quality of the National Accounts," Economic & Labour Market Review, Palgrave Macmillan;Office for National Statistics, vol. 2(3), pages 24-33, March.
    12. Ben Powell & Guy Nason & Duncan Elliott & Matthew Mayhew & Jennifer Davies & Joe Winton, 2018. "Tracking and modelling prices using web‐scraped price microdata: towards automated daily consumer price index forecasting," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(3), pages 737-756, June.
    13. Domenico Giannone & Lucrezia Reichlin & David Small, 2008. "Nowcasting: the real time informational content of macroeconomic data releases," ULB Institutional Repository 2013/6409, ULB -- Universite Libre de Bruxelles.
    14. Reichlin, Lucrezia & Giannone, Domenico & Small, David, 2005. "Nowcasting GDP and Inflation: The Real Time Informational Content of Macroeconomic Data Releases," CEPR Discussion Papers 5178, C.E.P.R. Discussion Papers.
    15. Cavallo, Alberto, 2013. "Online and official price indexes: Measuring Argentina's inflation," Journal of Monetary Economics, Elsevier, vol. 60(2), pages 152-165.
    16. Jan A. Brakel & Sabine Krieg, 2016. "Small area estimation with state space common factor models for rotating panels," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 179(3), pages 763-791, June.
    17. Charles F. Manski, 2014. "Communicating Uncertainty in Official Economic Statistics," NBER Working Papers 20098, National Bureau of Economic Research, Inc.
    18. Rastislav Potocký & Helmut Waldl & Milan Stehlík, 2014. "On Sums of Claims and their Applications in Analysis of Pension Funds and Insurance Products," Prague Economic Papers, Prague University of Economics and Business, vol. 2014(3), pages 349-370.
    19. Alberto Cavallo & Roberto Rigobon, 2016. "The Billion Prices Project: Using Online Prices for Measurement and Research," Journal of Economic Perspectives, American Economic Association, vol. 30(2), pages 151-178, Spring.
    20. Claudio Agostinelli & Andy Leung & Victor Yohai & Ruben Zamar, 2015. "Robust estimation of multivariate location and scatter in the presence of cellwise and casewise contamination," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 24(3), pages 441-461, September.
    21. Jan Beran & Dieter Schell & Milan Stehlík, 2014. "The harmonic moment tail index estimator: asymptotic distribution and robustness," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 66(1), pages 193-220, February.
    22. Li‐Chun Zhang, 2012. "Topics of statistical theory for register‐based statistics and data integration," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 66(1), pages 41-63, February.
    23. D. L. Oberski & A. Kirchner & S. Eckman & F. Kreuter, 2017. "Evaluating the Quality of Survey and Administrative Data with Generalized Multitrait-Multimethod Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(520), pages 1477-1489, October.
    24. Niels Keiding & Thomas A. Louis, 2016. "Perils and potentials of self-selected entry to epidemiological studies and surveys," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 179(2), pages 319-376, February.
    25. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881.
    26. Crook, Jonathan & Banasik, John, 2004. "Does reject inference really improve the performance of application scoring models?," Journal of Banking & Finance, Elsevier, vol. 28(4), pages 857-874, April.
    27. Alison Whitworth & Kirsten Piller & Angela Luna & Li-Chun Zhang, 2015. "Small area estimates of the population distribution by ethnic group in England: a proposal using structure preserving estimators," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 16(4), pages 585-602, December.
    28. Karr, A.F. & Kohnen, C.N. & Oganian, A. & Reiter, J.P. & Sanil, A.P., 2006. "A Framework for Evaluating the Utility of Data Altered to Protect Confidentiality," The American Statistician, American Statistical Association, vol. 60, pages 224-232, August.
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