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Suggested Citation:"COMPUTATIONS OF MICROSIMULATION MODELS." National Research Council. 1991. Improving Information for Social Policy Decisions -- The Uses of Microsimulation Modeling: Volume II, Technical Papers. Washington, DC: The National Academies Press. doi: 10.17226/1853.
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VARIANCE ESTIMATION OF MICROSIMULATION MODELS THROUGH SAMPLE REUSE 238 given the complexity of these models. However, recent advances in nonparametric variance estimation, including the development of Efron's bootstrap, and recent increases in computational power, have made possible the estimation of the variance of microsimulation model projections. Expected advances both in computational power and in further understanding of the application of the bootstrap and other techniques to variance estimation for complex models will facilitate the calculation of these estimates. Before discussing how the bootstrap and other variance estimation techniques might be applied to microsimulation models, this chapter presents introductions to both computations of microsimulation models and nonparametric variance estimation. COMPUTATIONS OF MICROSIMULATION MODELS Microsimulation models use as primary input major surveys, such as the Current Population Survey (CPS), or samples from administrative records, such as samples from federal individual income tax filings. For example, assume that Congress is considering a major change in AFDC. An obvious question is how much this change will cost. An estimate is derived by first determining how many of the individuals in the CPS sample would be eligible for AFDC, how many of those would choose to participate, and how much assistance the eligible participants would receive. Next, to estimate the cost of the proposed changes to AFDC, the same steps would be taken with eligibility and benefit calculations performed for the proposed program. Finally, since the CPS is a national sample, the differences for each sampled family would be weighted to estimate the difference nationally, as well as the effects on smaller demographic groups. However, this simple idea is complicated by several factors. First, the covariate data in the available survey sample are often not rich enough to determine eligibility, participation, or amount of assistance received. Therefore, the information for each record is often augmented, either by imputation, by exact matching to administrative records, or through the use of a statistical match. (For a definition and discussion of statistical matching, see Rodgers [1984]; see also Cohen [Ch. 2 in this volume].) Second, the data are in ma ny cases collected annually, while the programs of interest are administered monthly, so it is necessary to allocate income and other data to the households monthly. Third, most policy makers are interested in the effects due to a program change for several years into the future, not just the upcoming year. This is especially the case for attempts to use microsimulation modeling to estimate the costs of health insurance programs and the costs of modifications to social security and pension plans. In addition, by the time a data set is made available and the various adjustments to the original data set have been completed, the data are often 2 or 3 years old. These two factors have contributed to the development of aging procedures that either reweight the data to take into consideration external estimates of the expected demographic,

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This volume, second in the series, provides essential background material for policy analysts, researchers, statisticians, and others interested in the application of microsimulation techniques to develop estimates of the costs and population impacts of proposed changes in government policies ranging from welfare to retirement income to health care to taxes.

The material spans data inputs to models, design and computer implementation of models, validation of model outputs, and model documentation.

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