Projecting from Advance Data Using Propensity Modeling: An Application to Income and Tax Statistics
AbstractThis article proposes and evaluates two new methods of reweighting preliminary data to obtain estimates more closely approximating those derived from the final data set. In the authors' motivating example, the preliminary data are an early sample after all tax returns have been processed. The new methods estimate a predicted propensity for late filing for each return in the advance sample and then poststratify based on these propensity scores. Using advance and complete sample data for 1982, the authors demonstrate that the new methods produce advance estimates generally much close to the final estimates than those derived from the current advance estimation techniques. The results demonstrate the value of propensity modeling, a general-purpose methodology that can be applied to a wide range of problems, including adjustment for unit nonresponse and frame undercoverage as well as statistical matching. Coauthors are Sharon M. Hirabayashi, Roderick J. A. Little, and Donald B. Rubin.
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Bibliographic InfoArticle provided by American Statistical Association in its journal Journal of Business and Economic Statistics.
Volume (Year): 10 (1992)
Issue (Month): 2 (April)
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Web page: http://www.amstat.org/publications/jbes/index.cfm?fuseaction=main
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- Rajeev H. Dehejia & Sadek Wahba, 1998. "Propensity Score Matching Methods for Non-experimental Causal Studies," NBER Working Papers 6829, National Bureau of Economic Research, Inc.
- Rajeev H. Dehejia & Sadek Wahba, 2002. "Propensity score matching methods for non-experimental causal studies," Discussion Papers 0102-14, Columbia University, Department of Economics.
- Petreski, Marjan & Jovanovic, Branimir, 2013. "Do Remittances Reduce Poverty and Inequality in the Western Balkans? Evidence from Macedonia," MPRA Paper 51413, University Library of Munich, Germany.
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