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Improving Information for Social Policy Decisions -- The Uses of Microsimulation Modeling Volume II, Technical Papers (1991) / Chapter Skim
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Pages 247-249

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From page 247...
... This approach ignores the variability from the use of various control totals and regression equations, from the use of imputation and statistical matching, from the use of demographic and macroeconomic projections, and from the use of aging modules. These sources of variability are likely to dominate the variability due to sampling in the primary input data base (although the relative magnitude of the various sources of uncertainty is currently unknown)
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From page 248...
... First, microsimulation models reweight the original sample survey for a variety of purposes, especially to statically age the data, to account for undercoverage of populations, and, more generally, to reconcile margins to accepted control totals. In addition, microsimulation models make use of venous types of regression equations that have often been estimated on other data sets, for example, to estimate participation rates and impute variables not in the primary database.
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From page 249...
... Clearly, if one was making use of dozens or even hundreds of parameter estimates, the estimation of the covariance matrix becomes essentially impossible since the number of correlations needing to be estimated is on the order of n2. Since the use of this many control totals and regression coefficients is not that unusual for many microsimulation models, one would have to assume that a large number of the parameter estimates were either constant or independent, which might be reasonable in some situations.
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Key Terms

  • statistical match
  • sensitivity analysis
  • statistical matching
  • microsimulation models
  • variance estimate

  • simulation models
  • participation rates
  • confidence intervals
  • variability due
  • microsimulation model

  • control totals
  • input data
  • sample surveys
  • confidence interval
  • aging module

  • regression coefficients
  • eligible people
  • simulation model
  • constrained statistical
  • variance due

  • primary input
  • regression equations
  • normal distribution
  • random variables


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