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Testing the Statistical Significance of Microsimulation Results: Often Easier than You Think. A Technical Note

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TitleTesting the Statistical Significance of Microsimulation Results: Often Easier than You Think. A Technical Note
Publication TypeReport
Year of Publication2013
AuthorsGoedemé, T., Van den Bosch K., Salanauskaite L., & Verbist G.
Series TitleImPRovE Working Papers
Document NumberImPRovE Methodological paper 13/10
Pagination27
PublisherHerman Deleeck Centre for Social Policy (University of Antwerp)
Place PublishedAntwerp
Keywordscovariance, EU-SILC, EUROMOD, microsimulation, significance tests, Statistical inference, t-test
Abstract

In 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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