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A Robust Approach of Regression-Based Statistical Matching for Continuous Data
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 Title & Authors
A Robust Approach of Regression-Based Statistical Matching for Continuous Data
Sohn, Soon-Cheol; Jhun, Myoung-Shic;
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 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;
 Language
English
 Cited by
 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. crossref(new window)

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. crossref(new window)

5.
Jhun, M. and Choi, I. (2009). Bootstrapping least distance estimator in the multivariate regression model, Computational Statistics & Data Analysis, 53, 4221-4227. crossref(new window)

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. crossref(new window)

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. crossref(new window)

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. crossref(new window)

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.