Testing the Statistical Significance of Microsimulation Results: A Plea
In the microsimulation literature, it is still uncommon to test the statistical significance of results. In this article 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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- John Creedy & Guyonne Kalb & Hsein Kew, 2005.
"Confidence Intervals for Policy Reforms in Behavioural Tax Microsimulation Modelling,"
Department of Economics - Working Papers Series
936, The University of Melbourne.
- John Creedy & Guyonne Kalb & Hsein Kew, 2007. "Confidence Intervals For Policy Reforms In Behavioural Tax Microsimulation Modelling," Bulletin of Economic Research, Wiley Blackwell, vol. 59(1), pages 37-65, 01.
- John Creedy & Guyonne Kalb & Hsein Kew, 2004. "Confidence Intervals for Policy Reforms in Behavioural Tax Microsimulation Modelling," Melbourne Institute Working Paper Series wp2004n32, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
- Peter Ericson & Lennart Flood, 2012.
"A Microsimulation Approach to an Optimal Swedish Income Tax,"
International Journal of Microsimulation,
International Microsimulation Association, vol. 2(5), pages 2-21.
- Ericson, Peter & Flood, Lennart, 2009. "A Microsimulation Approach to an Optimal Swedish Income Tax," Working Papers in Economics 375, University of Gothenburg, Department of Economics.
- Ericson, Peter & Flood, Lennart, 2009. "A Microsimulation Approach to an Optimal Swedish Income Tax," IZA Discussion Papers 4379, Institute for the Study of Labor (IZA).
- Afshartous, David & Preston, Richard A., 2010. "Confidence intervals for dependent data: Equating non-overlap with statistical significance," Computational Statistics & Data Analysis, Elsevier, vol. 54(10), pages 2296-2305, October.
- Yves G. Berger & Chris J. Skinner, 2003. "Variance estimation for a low income proportion," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 52(4), pages 457-468.
- Atkinson, Tony & Cantillon, Bea & Marlier, Eric & Nolan, Brian, 2002. "Social Indicators: The EU and Social Inclusion," OUP Catalogue, Oxford University Press, number 9780199253494, December.
- Koen Decancq & Tim Goedemé & Karel Van den Bosch & Josefine Vanhille, 2013. "The Evolution of Poverty in the European Union: Concepts, Measurement and Data," ImPRovE Working Papers 13/01, Herman Deleeck Centre for Social Policy, University of Antwerp.
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