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STATISTICAL MATCHING AND MICROSIMULATION MODELS 83 matching plays in the creation of data sets that drive microsimulation models and other analyses. Today, statistical matching seems to be the only possibility for providing information about a large number of public policy issues in the current climate of restrictive budgets, protection of data confidentiality, and reduction of respondent burden. It is easy to dismiss statistical matching as a technique that makes unsupported assumptions about the covariance of YZ, which is of primary interest. However, rather than dismiss statistical matching, we need to consider ways in which it can be improved, at least until the climate changes. In the discussion above, some techniques have been offered, especially the use of sensitivity analyses and the use of auxiliary information, that might be available from small special studies, including small exact matches or small comprehensive surveys, or through the use of census information, from which the degree of conditional dependence can be estimated. (For a thorough discussion of ways in which auxiliary information might be used, see Singh et al. .) The increased use of sensitivity analysis would indicate the degree to which inferences might be compromised by the failure of the conditional independence assumption. The increased use of auxiliary information would permit the creation of data sets that better mimic the properties of the data set needed for analysis, and would likely facilitate a great deal of useful policy analysis that would be impossible today without this type of statistical matching. Armstrong (1989:44) states: The study also suggestsâ¦that auxiliary information about the distribution of (Y, Z) (obtained, for example, from a sample of (X, Y, Z) observations) is necessary to reduce distortion in the conditional distribution of (Y, Z) given X. Such an approach is likely to be able to better guide statistical matching. REFERENCES Alter, H. 1974 Creation of a synthetic data set by linking records of the Canadian survey of consumer finances with the family expenditure survey 1970. Annals of Economic and Social Measurement 3:373â394. Armstrong, J. 1989 An Evaluation of Statistical Matching Methods. Working paper no. BSMD 90â003E. Methodology Branch, Statistics Canada, Ottawa. 1990 Notes on âAn Evaluation of Statistical Matching Methods.â Unpublished manuscript. Statistics Canada, Ottawa. Barr, R.S., and Turner, J.S. 1978 A new, linear programming approach to microdata file merging. Pp. 131â149 in 1978 Compendium of Tax Research. Office of Tax Analysis. Washington, D.C.: U.S. Department of the Treasury. Bishop, Y.M.M., Fienberg, S.E., and Holland, P.W. 1975 Discrete Multivariate Analysis: Theory and Practice. Cambridge, Mass.: The MIT Press.
STATISTICAL MATCHING AND MICROSIMULATION MODELS 84 Brieman, L., Friedman, J.H., Olshen, R., and Stone, C.J. 1984 Classification and Regression Trees. Belmont, Calif.: Wadsworth. Bristol, R.B., Jr. 1988 Tax modelling and the policy environment of the 1990s. Pp. 115â122 in Statistics of Income Bulletin. 75th Anniversary Issue. Statistics of Income Division, Internal Revenue Service. Washington, D.C.: U.S. Department of the Treasury. Cilke, J.M., Nelson, S.C., and Wyscarver, R.A. 1988 The Tax Reform Data Base. Paper prepared for the Seventy- Ninth Annual Conference on Taxation, National Tax AssociationâTax Institute of America. Office of Tax Analysis, U.S. Department of the Treasury, Washington, D.C. Kadane, J.B. 1978 Statistical problems of merged data files. Paper 6 in Compilation of OTA Papers., Vol. 1. Washington, D.C.: U.S. Department of the Treasury. Klevmarken, N.A. 1982 Missing variables and two-stage least squares estimation from more than one data set. Pp. 156â161 in 1981 Proceedings of the Business and Economic Statistics Section. Washington, D.C.: American Statistical Association. Okner, B. 1972 Constructing a new data base from existing microdata sets: The 1966 merge file. Annals of Economic and Social Measurement 1:325â362. 1974 Data matching and merging: An overview. Annals of Economic and Social Measurement 3:347â352. Paass, G. 1985 Statistical record linkage methodology: State of the art and future prospects. Pp. 9.3â1 to 9.3â16 in Proceedings of the 100th Session of the International Statistical Institute. Amsterdam: International Statistical Institute. 1988 Stochastic Generation of a Synthetic Sample from Marginal Information. Paper presented to the Workshop on Microsimulation Modeling, Statistics of Income Division, Internal Revenue Service, U.S. Department of the Treasury, Washington, D.C. Radner, D.B. 1983 Adjusted estimates of the size distribution of family money income. Journal of Business and Economic Statistics 1:136â 146. Radner, D., Allen, R., Gonzalez, M., Jabine, T., and Muller, H. 1980 Report on Exact and Statistical Matching Techniques. Statistical Policy Working Paper 5. Subcommittee on Matching Techniques, Federal Committee on Statistical Methodology, Office of Federal Statistical Policy and Standards. Washington, D.C.: U.S. Department of Commerce. Rodgers, W.L. 1984 An evaluation of statistical matching. Journal of Business and Economic Statistics 2:91â102. Rubin, D.B. 1986 Statistical matching us ing file concatenation with adjusted weights and multiple imputations. Journal of Business and Economic Statistics 4:86â94. Sims, C.A. 1972 Comments and rejoinder. Annals of Economic and Social Measurement 1:343â345, 355â357. 1974 Comment. Annals of Economic and Social Measurement 3:395â397. 1978 Comment (on Kadane). Pp. 172â177 in 1978 Compendium of Tax Research. Office of Tax Analysis. Washington, D.C.: U.S. Department of the Treasury.
STATISTICAL MATCHING AND MICROSIMULATION MODELS 85 Singh, A.C. 1988 Log-linear Imputation. Working Paper 88â029E. Methodology Branch, Statistics Canada, Ottawa. Singh, A.C., Mantel, H., Kinack, M., and Rowe, G. 1990 On Methods of Statistical Matching With and Without Auxiliary Information. Unpublished technical paper. Statistics Canada, Ottawa. Springs, R., and Beebout, H. 1976 The 1973 Merged Space/AFDC File: A Statistical Match of Data from the 1970 Decennial Census and the 1973 AFDC Survey. Washington, D.C.: Mathematica Policy Research, Inc.
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