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Citations for "Missing-Data Adjustments in Large Surveys"

by Little, Roderick J A

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  1. Hendrik Jürges & Lars Thiel & Tabea Bucher-Koenen & Johannes Rausch & Morten Schuth & Axel Börsch-Supan, 2014. "Health, Financial Incentives, and Early Retirement: Micro-Simulation Evidence for Germany," NBER Chapters, in: Social Security Programs and Retirement Around the World: Disability Insurance Programs and Retirement National Bureau of Economic Research, Inc.
  2. Eric French & John Bailey Jones, 2001. "The effects of health insurance and self-insurance on retirement behavior," Working Paper Series WP-01-19, Federal Reserve Bank of Chicago.
  3. Rässler, Susanne & Schnell, Rainer, 2004. "Multiple imputation for unit-nonresponse versus weighting including a comparison with a nonresponse follow-up study," Discussion Papers 65/2004, Friedrich-Alexander-University Erlangen-Nuremberg, Chair of Statistics and Econometrics.
  4. S. Nazli Wasti, 2001. "Predictors of Trust in Buyer-Supplier Relations: A Contextual and Cultural Comparison of Japan and Turkey," CIRJE F-Series CIRJE-F-108, CIRJE, Faculty of Economics, University of Tokyo.
  5. Chuang, Emmeline & Wells, Rebecca, 2010. "The role of inter-agency collaboration in facilitating receipt of behavioral health services for youth involved with child welfare and juvenile justice," Children and Youth Services Review, Elsevier, vol. 32(12), pages 1814-1822, December.
  6. Seppo Laaksonen, 2003. "Alternative imputation techniques for complex metric variables," Journal of Applied Statistics, Taylor & Francis Journals, vol. 30(9), pages 1009-1020.
  7. Joachim R. Frick & Markus M. Grabka, 2007. "Item Non-response and Imputation of Annual Labor Income in Panel Surveys from a Cross-National Perspective," SOEPpapers on Multidisciplinary Panel Data Research 49, DIW Berlin, The German Socio-Economic Panel (SOEP).
  8. Siddique, Juned & Belin, Thomas R., 2008. "Using an Approximate Bayesian Bootstrap to multiply impute nonignorable missing data," Computational Statistics & Data Analysis, Elsevier, vol. 53(2), pages 405-415, December.
  9. Rasner, Anika & Frick, Joachim R. & Grabka, Markus M., 2013. "Statistical Matching of Administrative and Survey Data: An Application to Wealth Inequality Analysis," EconStor Open Access Articles, ZBW - German National Library of Economics, pages 192-224.
  10. Natalie Shlomo & Chris J. Skinner & Barry Schouten, 2012. "Estimation of an indicator of the representativeness of survey response," LSE Research Online Documents on Economics 39124, London School of Economics and Political Science, LSE Library.
  11. Miguel Székely & Nora Lustig & Martin Cumpa & José Antonio Mejía-Guerra, 2000. "Do We Know How Much Poverty There Is?," Research Department Publications 4239, Inter-American Development Bank, Research Department.
  12. Brownstone, David, 1997. "Multiple Imputation Methodology for Missing Data, Non-Random Response, and Panel Attrition," University of California Transportation Center, Working Papers qt2zd6w6hh, University of California Transportation Center.
  13. Luci Ellis & Jeremy Lawson & Laura Roberts-Thomson, 2003. "Housing Leverage in Australia," RBA Research Discussion Papers rdp2003-09, Reserve Bank of Australia.
  14. Schunk, Daniel, 2007. "The German SAVE survey: documentation and methodology," Sonderforschungsbereich 504 Publications 07-08, Sonderforschungsbereich 504, Universität Mannheim;Sonderforschungsbereich 504, University of Mannheim.
  15. Raymundo M. Campos-Vázquez, 2013. "Efectos de los ingresos no reportados en el nivel y tendencia de la pobreza laboral en México," Ensayos Revista de Economia, Universidad Autonoma de Nuevo Leon, Facultad de Economia, vol. 0(2), pages 23-54, November.
  16. Bollinger, Christopher R. & Hirsch, Barry, 2010. "Is Earnings Nonresponse Ignorable?," IZA Discussion Papers 5347, Institute for the Study of Labor (IZA).
  17. Grabka, Markus & Westermeier, Christian, 2014. "Estimating the Impact of Alternative Multiple Imputation Methods on Longitudinal Wealth Data," Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100353, Verein für Socialpolitik / German Economic Association.
  18. Verbeek, M. & Nijman, T., 1992. "Incomplete Panels and Selection Bias: A Survey," Papers 9207, Tilburg - Center for Economic Research.
  19. Martin, Eisele & Zhu, Junyi, 2013. "Multiple imputation in a complex household survey - the German Panel on Household Finances (PHF): challenges and solutions," MPRA Paper 57666, University Library of Munich, Germany.
  20. Frank Potter & Eric Grau & John Czajka & Dan Scheer & Mark Levitan, 2010. "Imputation Variance Estimation Protocols for the NAS Poverty Measure The New York City Poverty Measure Experience," Mathematica Policy Research Reports 77be49e0f91f41e888de5139e, Mathematica Policy Research.
  21. Michael D. Hurd, 1998. "Anchoring Effects in the HRS: Experimental and Nonexperimental Evidence," NBER Technical Working Papers 0219, National Bureau of Economic Research, Inc.
  22. Joost Ginkel & Pieter Kroonenberg, 2014. "Using Generalized Procrustes Analysis for Multiple Imputation in Principal Component Analysis," Journal of Classification, Springer, vol. 31(2), pages 242-269, July.
  23. F. Di Lascio & Simone Giannerini & Alessandra Reale, 2015. "Exploring copulas for the imputation of complex dependent data," Statistical Methods and Applications, Springer, vol. 24(1), pages 159-175, March.
  24. Steven J. Haider & Melvin Stephens, 2007. "Is There a Retirement-Consumption Puzzle? Evidence Using Subjective Retirement Expectations," The Review of Economics and Statistics, MIT Press, vol. 89(2), pages 247-264, May.
  25. Gabriele Beissel Durrant, 2009. "Imputation Methods for Handling Item-Nonresponse in the Social Sciences: A Methodological Review," Working Papers id:2007, eSocialSciences.
  26. Christopher R. Bollinger & Barry T. Hirsch, 2010. "GDP & Beyond – die europäische Perspektive," Working Paper Series of the German Council for Social and Economic Data 165, German Council for Social and Economic Data (RatSWD).
  27. Juned Siddique & Ofer Harel, . "MIDAS: A SAS Macro for Multiple Imputation Using Distance-Aided Selection of Donors," Journal of Statistical Software, American Statistical Association, vol. 29(i09).
  28. Gianni La Cava & John Simon, 2003. "A Tale of Two Surveys: Household Debt and Financial Constraints in Australia," RBA Research Discussion Papers rdp2003-08, Reserve Bank of Australia.
  29. Michael Hurd & Elaine Reardon, 2003. "Real Wealth Changes from 1982 to 1991 Among the Newly Retired," Working Papers 03-15, RAND Corporation Publications Department.
  30. repec:mpr:mprres:6788 is not listed on IDEAS
  31. Dang, Hai-Anh H. & Lanjouw, Peter F. & Serajuddin, Umar, 2014. "Updating poverty estimates at frequent intervals in the absence of consumption data : methods and illustration with reference to a middle-income country," Policy Research Working Paper Series 7043, The World Bank.
  32. Gabriele Beissel-Durrant & Chris Skinner, 2003. "Estimation of the Distribution of Hourly Pay from Household Survey Data: The Use of Missing Data Methods to Handle Measurement Error," CeMMAP working papers CWP12/03, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  33. Joachim Inkmann, 2001. "Accounting for Nonresponse Heterogeneity in Panel Data," CoFE Discussion Paper 01-03, Center of Finance and Econometrics, University of Konstanz.
  34. Miguel Székely & Nora Lustig & Martin Cumpa & José Antonio Mejía-Guerra, 2000. "¿Sabemos qué tanta pobreza hay?," Research Department Publications 4240, Inter-American Development Bank, Research Department.
  35. Rässler, Susanne & Koller, Florian & Mäenpää, Christine, 2002. "A split questionnaire survey design applied to German media and consumer surveys," Discussion Papers 42b/2002, Friedrich-Alexander-University Erlangen-Nuremberg, Chair of Statistics and Econometrics.
  36. Olinsky, Alan & Chen, Shaw & Harlow, Lisa, 2003. "The comparative efficacy of imputation methods for missing data in structural equation modeling," European Journal of Operational Research, Elsevier, vol. 151(1), pages 53-79, November.
This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.