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Projecting from Advance Data Using Propensity Modeling: An Application to Income and Tax Statistics

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  • Czajka, John L, et al

Abstract

This 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.

Suggested Citation

  • Czajka, John L, et al, 1992. "Projecting from Advance Data Using Propensity Modeling: An Application to Income and Tax Statistics," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(2), pages 117-131, April.
  • Handle: RePEc:bes:jnlbes:v:10:y:1992:i:2:p:117-31
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    Cited by:

    1. Amang Sukasih & Donsig Jang & Sonya Vartivarian & Stephen Cohen & Fan Zhang, 2009. "A Simulation Study to Compare Weighting Methods for Survey Nonresponses in the National Survey of Recent College Graduates," Mathematica Policy Research Reports 613f000cac94492f91b53813f, Mathematica Policy Research.
    2. 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.
    3. Rajeev H. Dehejia & Sadek Wahba, 2002. "Propensity Score-Matching Methods For Nonexperimental Causal Studies," The Review of Economics and Statistics, MIT Press, vol. 84(1), pages 151-161, February.

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