Testing the Statistical Significance of Microsimulation Results: Often Easier than You Think. A Technical Note
AbstractIn the microsimulation literature, it is still uncommon to test the statistical significance of results. In this note we argue that this situation is both undesirable and unnecessary. Provided the parameters used in the microsimulation are exogenous, as is often the case in static microsimulation of the first-order effects of policy changes, simple statistical tests can be sufficient. Moreover, standard routines have been developed which enable applied researchers to calculate the sampling variance of microsimulation results, while taking the sample design into account, even of relatively complex statistics such as relative poverty, inequality measures and indicators of polarization, with relative ease and a limited time investment. We stress that when comparing simulated and baseline variables, as well as when comparing two simulated variables, it is crucial to take account of the covariance between those variables. Due to this covariance, the mean difference between the variables can generally (though not always) be estimated with much greater precision than the means of the separate variables.
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Bibliographic InfoPaper provided by Herman Deleeck Centre for Social Policy, University of Antwerp in its series ImPRovE Working Papers with number 13/10.
Date of creation: Aug 2013
Date of revision:
Statistical inference; significance tests; microsimulation; covariance; t-test; EUROMOD;
Find related papers by JEL classification:
- I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty
- D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
- I38 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Government Programs; Provision and Effects of Welfare Programs
- C - Mathematical and Quantitative Methods
- C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
- C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
- C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
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