[
Albayrak, O., and T. Masterson. 2017. Quality of statistical match of household budget survey and SILC for Turkey, Levy Economics Institute, Working Paper (885). DOI: https://doi.org/10.2139/ssrn.2924849.10.2139/ssrn.2924849
]Search in Google Scholar
[
Andridge, R.R., and R.J.A. Little. 2009, “The Use of Sample Weights in Hot Deck Imputation”. Journal of Official Statistics 25(1): 21–36. Available at: https://www.scb.se/contentassets/ca21efb41fee47d293bbee5bf7be7fb3/the-use-of-sample-weights-inhot-deck-imputation.pdf (accessed March 2022).
]Search in Google Scholar
[
Andridge, R.R., and R.J.A. Little. 2010. “A review of hot deck imputation for survey non-response”. International statistical review 78(1): 40–64. DOI: https://doi.org/10.1111/j.1751-5823.2010.00103.x.10.1111/j.1751-5823.2010.00103.x313033821743766
]Search in Google Scholar
[
Beretta, L. and A. Santaniello. 2016. “Nearest neighbor imputation algorithms: a critical evaluation”. BMC medical informatics and decision making 16(3): 74. DOI: https://doi.org/10.1186/s12911-016-0318-z.10.1186/s12911-016-0318-z495938727454392
]Search in Google Scholar
[
Burgette, L.F., and J.P. Reiter. 2010 “Multiple imputation for missing data via sequential regression trees”. American Journal of Epidemiology 172(9): 1070–1076. DOI: https://doi.org/10.1093/aje/kwq260.10.1093/aje/kwq26020841346
]Search in Google Scholar
[
Chen, J., and J. Shao. 2000. “Nearest Neighbor Imputation for Survey Data”. Journal of Official Statistics 16(2): 113–131. Available at: https://www.scb.se/contentassets/ca21efb41fee47d293bbee5bf7be7fb3/nearest-neighbor-imputation-for-survey-data.pdf.
]Search in Google Scholar
[
Conti, P.L., D. Marella, and M. Scanu 2012. “Uncertainty analysis in statistical matching”. Journal of Official Statistics 28(1): 69–88. Available at: https://www.scb.se/contentassets/ca21efb41fee47d293bbee5bf7be7fb3/uncertainty-analysis-in-statistical-matching.pdf.
]Search in Google Scholar
[
Dalla Chiara, E., Menon, M., and F. Perali, F. 2019. “An Integrated Database to Measure Living Standards”. Journal of Official Statistics 35(3): 531–576. DOI: https://doi.org/10.2478/JOS-2019-0023.10.2478/jos-2019-0023
]Search in Google Scholar
[
Donatiello, G., M. D’Orazio, D. Frattarola, A. Rizzi, M. Scanu, and M. Spaziani. 2014. “Statistical matching of income and consumption expenditures”. International Journal of Economic Sciences 3(3): 50–65.
]Search in Google Scholar
[
D’Orazio, M. 2020. Statmatch: Statistical matching or data fusion: R-package. Available at: https://cran.r-project.org/web/packages/StatMatch/StatMatch.pdf (accessed September 2021).
]Search in Google Scholar
[
D’Orazio, M., M. Di Zio, and M. Scanu. 2006a. Statistical matching: Theory and practice, John Wiley & Sons.10.1002/0470023554
]Search in Google Scholar
[
D’Orazio, M., M. Di Zio, and M. Scanu. 2006b. “Statistical Matching for Categorical Data: Displaying Uncertainty and Using Logical Constraints”. Journal of Official Statistics 22(1): l37–157. Available at: https://www.scb.se/contentassets/ca21efb41-fee47d293bbee5bf7be7fb3/statistical-matching-for-categorical-data-displaying-uncertainty-and-using-logical-constraints.pdf.
]Search in Google Scholar
[
D’Orazio, M., Frattarola, D., A. Rizzi, A., M. Scanu, and M. Spaziani. 2018, The statistical matching of EU-SILC and HBS at ISTAT: where do we stand for the production of official statistics. Available at: https://www.istat.it/it/les//2018/11/Scanuoriginal-paper.pdf (accessed September 2021).
]Search in Google Scholar
[
Endres, E., P. Fink, and T. Augustin. 2019. “Imprecise Imputation: A Nonparametric Micro Approach Reecting the Natural Uncertainty of Statistical Matching with Categorical Data”. Journal of Official Statistics 35(3): 599–624. DOI: http://doi.org/10.2478/JOS-2019-0025.10.2478/jos-2019-0025
]Search in Google Scholar
[
EU-SILC SUF DE. 2015. European union statistics on income and living conditions. Scientific use file Germany. Available at: https://ec.europa.eu/eurostat/cros/EU-SILCSUF_en.
]Search in Google Scholar
[
EU-SILC SUF FR. 2015. European union statistics. Scientific use file France. Available at: https://ec.europa.eu/eurostat/cros/EU-SILC-SUF_en.
]Search in Google Scholar
[
Eurostat. 2013. European household income by groups of households. Available at: https://ec.europa.eu/eurostat/documents/3888793/5858173/KS-RA-13-023-EN.PDF (accessed September 2021).
]Search in Google Scholar
[
Eurostat. 2016. Methodological Guidelines and Description of EU-SILC Target Variables: DocSILC065, 2015 Operation. Available at: https://circabc.europa.eu/sd/a/afb4601b-4e5c-4f40-86bb-0c3d0d94aa12/DOCSILC065operation2015VERSION08-08-2016.pdf.
]Search in Google Scholar
[
Eurostat. 2018. R code to match EU-SILC and HBS.
]Search in Google Scholar
[
Fosdick, B.K., M. DeYoreo, and J.P. Reiter. 2016. “Categorical data fusion using auxiliary information”. The Annals of Applied Statistics 10(4): 1907–1929. DOI: https://doi.org/10.1214/16-AOAS925.10.1214/16-AOAS925
]Search in Google Scholar
[
Gabler, S. 1997. “Datenfusion”. ZUMA-Nachrichten 21(40): 81–92.
]Search in Google Scholar
[
Gilula, Z., R.E. McCulloch, and P.E. Rossi. 2006. “A direct approach to data fusion”. Journal of Marketing Research 43(1): 73–83. DOI: https://doi.org/10.1509/jmkr.43.1.73.10.1509/jmkr.43.1.73
]Search in Google Scholar
[
Gower, J.C. 1971. “A general coefficient of similarity and some of its properties”. Biometrics 27(4): 857–871. DOI: https://doi.org/10.2307/2528823.10.2307/2528823
]Search in Google Scholar
[
Kamakura, W.A., and M. Wedel. 1997. “Statistical data fusion for cross-tabulation”. Journal of Marketing Research 34(4): 485–498. DOI: https://doi.org/10.1177/002224379703400406.10.1177/002224379703400406
]Search in Google Scholar
[
Kiesl, H., and S. Rässler. 2005. “Techniken und Einsatzgebiete von Datenintegration und Datenfusion”. In Datenfusion und Datenintegration: 6. Wissenschaftliche Tagung, Tagungsberichte, Bonn: 17–32.
]Search in Google Scholar
[
Kiesl, H., and S. Rässler. 2006. How valid can data fusion be? Available at: http://doku.iab.de/discussionpapers/2006/dp1506.pdf (accessed September 2021).
]Search in Google Scholar
[
Kim, J.K. 2002. “A note on approximate bayesian bootstrap imputation”. Biometrika 89(2): 470–477. DOI: https://doi.org/10.1093/biomet/89.2.470.10.1093/biomet/89.2.470
]Search in Google Scholar
[
Kleinke, K. 2017. “Multiple imputation under violated distributional assumptions: A systematic evaluation of the assumed robustness of predictive mean matching”. Journal of Educational and Behavioral Statistics 42(4): 371–404. DOI: https://doi.org/10.3102/1076998616687084.10.3102/1076998616687084
]Search in Google Scholar
[
Koller-Meinfelder, F. 2009. Analysis of Incomplete Survey Data – Multiple Imputation via Bayesian Bootstrap Predictive Mean Matching. PhD thesis, Bamberg. Available at: https://fis.uni-bamberg.de/bitstream/uniba/213/2/Dokument_1.pdf.
]Search in Google Scholar
[
Koschnick, W.J. 1995. Standard-Lexikon für Mediaplanung und Mediaforschung in Deutschland: Bd. 1.2, 2., überarb. aufl. edn, Saur, München.
]Search in Google Scholar
[
Lamarche, P. 2017. Measuring Income, Consumption and Wealth jointly at the Micro-Level. Eurostat. Available at: https://ec.europa.eu/eurostat/documents/7894008/8074103/income_methodological_note.pdf.
]Search in Google Scholar
[
Lamarche, P. 2018. Measuring Income, Consumption and Wealth jointly at the microlevel. Eurostat.
]Search in Google Scholar
[
Landerman, L.R., K.C. Land, and C.F. Pieper. 1997. “An empirical evaluation of the predictive mean matching method for imputing missing values”. Sociological Methods & Research 26(1): 3–33. DOI: https://doi.org/10.1177/0049124197026001001.10.1177/0049124197026001001
]Search in Google Scholar
[
Leulescu, A. and M. Agafitei. 2013, Statistical matching: A model based approach for data integration. Available at: https://ec.europa.eu/eurostat/documents/3888793/5855821/KS-RA-13-020-EN.PDF (accessed September 2021).
]Search in Google Scholar
[
Little, R.J.A. 1988. “Missing-data adjustments in large surveys”. Journal of Business & Economic Statistics 6(3): 287–296.10.1080/07350015.1988.10509663
]Search in Google Scholar
[
Little, R.J.A., and D.B. Rubin. 2020. Statistical analysis with missing data, third edition, John Wiley & Sons.10.1002/9781119482260
]Search in Google Scholar
[
Lumley, T., and A. Miller. 2020. leaps: Regression subset selection: R-package. Available at: https://cran.r-project.org/web/packages/leaps/leaps.pdf (accessed September 2021).
]Search in Google Scholar
[
Meinfelder, F. 2013. “Datenfusion: Theoretische implikationen und praktische umsetzung”. In Weiterentwicklung der amtlichen Haushaltsstatistiken, edited by T. Riede, N. Ott, S. Bechthold, T. Schmidt, M. Eisele, B. Schimpl-Neimanns, F. Meinfelder, R. MŁunnich, J.P. Burgard and T. Zimmermann: 83–98.
]Search in Google Scholar
[
Meinfelder, F., and T. Schnapp. 2015. Baboon: Bayesian bootstrap predictive mean matching – multiple and single imputation for discrete data: R-package. Available at: https://cran.r-project.org/web/packages/BaBooN/BaBooN.pdf (accessed September 2021).
]Search in Google Scholar
[
Meng, X.-L. 1994. “Multiple-imputation inferences with uncongenial sources of input”. Statistical Science 9(4): 538–558. DOI: https://doi.org/10.1214/ss/1177010269.10.1214/ss/1177010269
]Search in Google Scholar
[
Okner, B. 1972. “Constructing a new data base from existing microdata sets: The 1966 merge file”. In Annals of Economic and Social Measurement, 3(1): 325–362, National Bureau of Economic Research, Inc.
]Search in Google Scholar
[
Parzen, M., Lipsitz, S.R., and G.M. Fitzmaurice. 2005. “A note on reducing the bias of the approximate bayesian bootstrap imputation variance estimator”. Biometrika 92(4): 971–974. DOI: https://doi.org/10.1093/biomet/92.4.971.10.1093/biomet/92.4.971
]Search in Google Scholar
[
Pfeffermann, D., and A. Sikov. 2011. “Imputation and Estimation under Nonignorable Nonresponse in Household Surveys with Missing Covariate Information”. Journal of Official Statistics 27(2): 181–209. Available at: https://www.scb.se/contentassets/-ca21efb41fee47d293bbee5bf7be7fb3/imputation-and-estimation-under-nonignorablenonresponse-in-household-surveys-with-missing-covariate-information.pdf (accessed March 2022).
]Search in Google Scholar
[
Quartagno, M., J.R. Carpenter, and H. Goldstein. 2020. “Multiple imputation with survey weights: a multilevel approach”. Journal of Survey Statistics and Methodology 8(5): 965–989. DOI: https://doi.org/10.1093/jssam/smz036.10.1093/jssam/smz036
]Search in Google Scholar
[
R Core Team. 2021. R: A Language and Environment for Statistical Computing, R. Foundation for Statistical Computing, Vienna, Austria. Available at: https://www.R-project.org/ (accessed September 2021).
]Search in Google Scholar
[
Rässler, S. 2002. “Statistical matching: A frequentist theory, practical applications, and alternative Bayesian approaches”. Vol. 168 of Lecture notes in statistics, Springer, New York.10.1007/978-1-4613-0053-3_2
]Search in Google Scholar
[
Rodgers, W.L. 1984. “An evaluation of statistical matching”. Journal of Business & Economic Statistics 2: 91–102. DOI: https://doi.org/10.2307/1391358.10.2307/1391358
]Search in Google Scholar
[
Rubin, D.B. 1978. “Multiple imputation in sample surveys – a phenomological bayesian approach to nonresponse”. In Proceedings of the Survey Research Method Section of the American Statistical Association: 20–40. Available at: http://www.asasrms.org/GGTSPU-f422b6f0b7825427-56279-110474-QWt4FYDtNN9fK3kX-LOD/Proceedings/papers/1978_004.pdf.
]Search in Google Scholar
[
Rubin, D.B. 1986. “Statistical matching using file concatenation with adjusted weights and multiple imputations”. Journal of Business & Economic Statistics 4(1): 87–94.10.1080/07350015.1986.10509497
]Search in Google Scholar
[
Rubin, D.B. 1987. Multiple Imputation for Nonresponse in Surveys, Wiley, New York.10.1002/9780470316696
]Search in Google Scholar
[
Rubin, D.B., and N. Schenker. 1986. “Multiple imputation for interval estimation from simple random samples with ignorable nonresponse”. Journal of the American Statistical Association 81(394): 366–374. DOI: https://doi.org/10.2307/1391390.10.2307/1391390
]Search in Google Scholar
[
Serafino, P., and R. Tonkin. 2017. Statistical Matching of European Union Statistics on Income and Living Conditions (EU-SILC) and the Household Budget Survey, Eurostat. Available at:. https://ec.europa.eu/eurostat/documents/3888793/7882299/KS-TC-16-026-ENN.pdf (accessed September 2021).
]Search in Google Scholar
[
Sims, C.A. 1972. “Comments (on Okner 1972)”. Annals of Economic and Social Measurement 1: 343–345.
]Search in Google Scholar
[
Singh, A.C., H.J. Mantel, M.D. Kinack, and G. Rowe. 1993. “Statistical matching: Use of auxiliary information as an alternative to the conditional independence assumption”. Survey Methodology 19(1): 59–79.
]Search in Google Scholar
[
Stiglitz, J., Sen, A., and J. Fitoussi. 2009. Report of the Commission on the Measurement of Economic Performance and Social Progress (CMEPSP). Avasilable at: https://ec.europa.eu/eurostat/documents/8131721/8131772/Stiglitz-Sen-Fitoussi-Commission-report.pdf.
]Search in Google Scholar
[
Uçar, B., and G. Betti. 2016. Longitudinal statistical matching: transferring consumption expenditure from hbs to silc panel survey, Technical report, Department of Economics, University of Siena. Available at: http://repec.deps.unisi.it/quaderni/739.pdf.
]Search in Google Scholar
[
Van Buuren, S. 2018. Flexible imputation of missing data, CRC press.10.1201/9780429492259
]Search in Google Scholar
[
Van Buuren, S. 2021. Mice: Multivariate imputation by chained equations: R-package. Available at: https://cran.r-project.org/web/packages/mice/mice.pdf (accessed September 2021).
]Search in Google Scholar
[
Van Buuren, S. and K. Groothuis-Oudshoorn. 2011. “Mice: Multivariate imputation by chained equations in r”. Journal of Statistical Software 45(3): l–67.10.18637/jss.v045.i03
]Search in Google Scholar
[
Van der Putten, P., Kok, J.N., and A. Gupta. 2002. Data fusion through statistical matching: Working paper 4342-02, MIT Sloan School of Management. DOI: http://doi.org/10.2139/ssrn.297501.10.2139/ssrn.297501
]Search in Google Scholar
[
Webber, D. and R. Tonkin. 2013. Statistical Matching of EU-SILC and the Household Budget Survey to Compare Poverty Estimates Using Income, Expenditures and Material Deprivation, Eurostat. Available: https://ec.europa.eu/eurostat/documents/3888793/5857145/KS-RA-13-007-EN.PDF (accessed September 2021).
]Search in Google Scholar
[
Xie, X. and X.-L. Meng. 2017. “Dissecting multiple imputation from a multi-phase inference perspective: What happens when god’s, imputer’s and analyst’s models are uncongenial?”. Statistica Sinica: l485–1545. DOI: https://doi.org/10.5705/ss.2014.067.10.5705/ss.2014.067
]Search in Google Scholar
[
Zhang, L.-C. 2015. “On Proxy Variables and Categorical Data Fusion”. Journal of Official Statistics 31(4): 783–807. DOI: http://doi.org/10.1515/JOS-2015-0045.10.1515/jos-2015-0045
]Search in Google Scholar
[
Zhou, H. 2014. Accounting for Complex Sample Designs in Multiple Imputation Using the Finite Population Bayesian Bootstrap, PhD thesis, Michigan. DOI: https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.911.6156&rep=rep1&type=pdf.
]Search in Google Scholar