Testing the Statistical Significance of Microsimulation Results: A Plea
AbstractIn 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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Bibliographic InfoArticle provided by Interational Microsimulation Association in its journal International Journal of Microsimulation.
Volume (Year): 6 (2013)
Issue (Month): 3 ()
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Web page: http://ima.natsem.canberra.edu.au/index.htm
Microsimulation; statistical inference; EUROMOD.;
Find related papers by JEL classification:
- I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty
- I38 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Government Programs; Provision and Effects of Welfare Programs
- D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
- C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
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- John Creedy & Guyonne Kalb & Hsein Kew, 2004.
"Confidence Intervals for Policy Reforms in Behavioural Tax Microsimulation Modelling,"
Melbourne Institute Working Paper Series, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne
wp2004n32, Melbourne Institute of Applied Economic and Social Research, 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, Wiley Blackwell, vol. 59(1), pages 37-65, 01.
- John Creedy & Guyonne Kalb & Hsein Kew, 2005. "Confidence Intervals for Policy Reforms in Behavioural Tax Microsimulation Modelling," Department of Economics - Working Papers Series, The University of Melbourne 936, The University of Melbourne.
- Ericson, Peter & Flood, Lennart, 2009.
"A Microsimulation Approach to an Optimal Swedish Income Tax,"
Working Papers in Economics, University of Gothenburg, Department of Economics
375, University of Gothenburg, Department of Economics.
- Peter Ericson & Lennart Flood, 2012. "A Microsimulation Approach to an Optimal Swedish Income Tax," International Journal of Microsimulation, Interational Microsimulation Association, Interational Microsimulation Association, vol. 2(5), pages 2-21.
- 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).
- Atkinson, Tony & Cantillon, Bea & Marlier, Eric & Nolan, Brian, 2002. "Social Indicators: The EU and Social Inclusion," OUP Catalogue, Oxford University Press, Oxford University Press, number 9780199253494, October.
- 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, Herman Deleeck Centre for Social Policy, University of Antwerp 13/01, Herman Deleeck Centre for Social Policy, University of Antwerp.
- 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, Royal Statistical Society, vol. 52(4), pages 457-468.
- Afshartous, David & Preston, Richard A., 2010. "Confidence intervals for dependent data: Equating non-overlap with statistical significance," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 54(10), pages 2296-2305, October.
- Manos Matsaganis & Chrysa Leventi, 2014. "Distributive Effects of the Crisis and Austerity in Seven EU Countries," ImPRovE Working Papers, Herman Deleeck Centre for Social Policy, University of Antwerp 14/04, Herman Deleeck Centre for Social Policy, University of Antwerp.
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