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A Robust Approach of Regression-Based Statistical Matching for Continuous Data

Sohn, Soon-Cheol;Jhun, Myoung-Shic

  • Received : 2012.02.04
  • Accepted : 2012.03.30
  • Published : 2012.04.30

Abstract

Statistical matching is a methodology used to merge microdata from two (or more) files into a single matched file, the variants of which have been extensively studied. Among existing studies, we focused on Moriarity and Scheuren's (2001) method, which is a representative method of statistical matching for continuous data. We examined this method and proposed a revision to it by using a robust approach in the regression step of the procedure. We evaluated the efficiency of our revised method through simulation studies using both simulated and real data, which showed that the proposed method has distinct advantages over existing alternatives.

Keywords

Donor file;recipient file;matched file;common variable;unique variable;statistical matching

References

  1. Bai, Z. D., Chen, X. R., Miao, B. Q. and Rao, C. R. (1990). Asymptotic theory of least distance estimate in multivariate linear model, Statistics, 21, 503-519. https://doi.org/10.1080/02331889008802260
  2. Bertsekas, D. P. (1991). Linear Network Optimization: Algorithms and Codes, Massachusetts: MIT Press, Cambridge.
  3. Bertsekas, D. P. and Tseng, P. (1994). RELAX-IV: A faster version of the RELAX code for solving minimum cost flow problems. Available on the Internet at http://web.mit.edu/dimitrib/www/home.html.
  4. Harrison, D. and Rubinfeld, D. L. (1978). Hedonic prices and the demand for clean air, Journal of Environmental Economics and Management, 5, 81-102. https://doi.org/10.1016/0095-0696(78)90006-2
  5. Jhun, M. and Choi, I. (2009). Bootstrapping least distance estimator in the multivariate regression model, Computational Statistics & Data Analysis, 53, 4221-4227. https://doi.org/10.1016/j.csda.2009.05.012
  6. Kadane, J. B. (1978). Some statistical problems in merging data files. 1978 Compendium of Tax Research, U.S. Department of the Treasury, 159-171. (Reprinted in Journal of Official Statistics, 17, 423-433).
  7. Moriarity, C. and Scheuren, F. (2001). Statistical matching: a paradigm for assessing the uncertainty in the procedure, Journal of Official Statistics, 17, 407-422.
  8. Moriarity, C. and Scheuren, F. (2003). A note on Rubin's statistical matching using file concatenation with adjusted weights and multiple imputaions, Journal of Business & Economic Statistics, 21, 65-73. https://doi.org/10.1198/073500102288618766
  9. Okner, B. (1972). Constructing a new data base from existing micro-data sets: The 1966 merge file, Annals of Economic and Social Measurement, 1, 325-362.
  10. Okner, B. (1974). Data matching and merging: An overview, Annals of Economic and Social Measurement, 3, 347-352.
  11. Paass, G. (1982). Statistical match with additional information. Internal Report IPES.82.0204, Gesellschaft fur Mathematik und Datenverarbeitung, Bonn, W. Ger.
  12. Rodgers, W. L. (1984). An evaluation of statistical matching, Journal of Business & Economic Statistics, 2, 91-102. https://doi.org/10.2307/1391358
  13. Rubin, D. B. (1986). Statistical matching using file concatenation with adjusted weights and multiple imputations, Journal of Business and Economic Statistics, 4, 87-94. https://doi.org/10.2307/1391390
  14. Sims, C. A. (1972). Comments and rejoinder, Annals of Economic and Social Measurement, 1, 343-345;355-357.
  15. Sims, C. A. (1974). Comment, Annals of Economic and Social Measurement, 3, 395-397.

Acknowledgement

Supported by : National Research Foundation of Korea(NRF)