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

Author

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  • Tim Goedemé
  • Karel Van den Bosch
  • Lina Salanauskaite
  • Gerlinde Verbist

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.

Suggested Citation

  • Tim Goedemé & Karel Van den Bosch & Lina Salanauskaite & Gerlinde Verbist, 2013. "Testing the Statistical Significance of Microsimulation Results: Often Easier than You Think. A Technical Note," ImPRovE Working Papers 13/10, Herman Deleeck Centre for Social Policy, University of Antwerp.
    • Handle: RePEc:hdl:improv:1310
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    References listed on IDEAS

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    Cited by:

    1. Leventi, Chrysa & Rastrigina, Olga & Sutherland, Holly, 2015. "Nowcasting: estimating developments in the risk of poverty and income distribution in 2013 and 2014," EUROMOD Working Papers EM12/15, EUROMOD at the Institute for Social and Economic Research.
    2. Jekaterina Navicke & Olga Rastrigina & Holly Sutherland, 2014. "Nowcasting Indicators of Poverty Risk in the European Union: A Microsimulation Approach," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 119(1), pages 101-119, October.
    3. Leventi, Chrysa & Rastrigina, Olga & Sutherland, Holly & Vujackov, Sanja, 2016. "Nowcasting: estimating developments in median household income and risk of poverty in 2014 and 2015," EUROMOD Working Papers EM8/16, EUROMOD at the Institute for Social and Economic Research.
    4. Fidel Picos & Marie-Luise Schmitz, 2016. "In-depth analysis of tax reforms using the EUROMOD microsimulation model," JRC Working Papers on Taxation & Structural Reforms 2016-06, Joint Research Centre.

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    More about this item

    Keywords

    Statistical inference; significance tests; microsimulation; covariance; t-test; EUROMOD;
    All these keywords.

    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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