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

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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.

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Bibliographic Info

Paper provided by Herman Deleeck Centre for Social Policy, University of Antwerp in its series ImPRovE Working Papers with number 13/10.

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Date of creation: Aug 2013
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Handle: RePEc:hdl:improv:1310

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Keywords: Statistical inference; significance tests; microsimulation; covariance; t-test; EUROMOD;

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  1. Klevmarken, N. Anders, 1998. "Statistical Inference in Micro Simulation Models: Incorporating external information," Working Paper Series 1998:20, Uppsala University, Department of Economics.
  2. Atkinson, Tony & Cantillon, Bea & Marlier, Eric & Nolan, Brian, 2002. "Social Indicators: The EU and Social Inclusion," OUP Catalogue, Oxford University Press, number 9780199253494, September.
  3. 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.
  4. 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.
  5. Immervoll, Herwig & Kleven, Henrik Jacobsen & Kreiner, Claus Thustrup & Saez, Emmanuel, 2005. "Welfare Reform in European Countries: A Microsimulation Analysis," IZA Discussion Papers 1810, Institute for the Study of Labor (IZA).
  6. repec:ese:emodwp:em11-13 is not listed on IDEAS
  7. repec:ese:emodwp:em6-09 is not listed on IDEAS
  8. Holly Sutherland & Francesco Figari, 2013. "EUROMOD: the European Union tax-benefit microsimulation model," International Journal of Microsimulation, Interational Microsimulation Association, vol. 1(6), pages 4-26.
  9. Howes, Stephen & Lanjouw, Jean Olson, 1998. "Does Sample Design Matter for Poverty Rate Comparisons?," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 44(1), pages 99-109, March.
  10. repec:ese:emodwp:em3-08 is not listed on IDEAS
  11. Robert Tanton & Yogi Vidyattama & Justine McNamara & Quoc Ngu Vu & Ann Harding, 2009. "Old, Single and Poor: Using Microsimulation and Microdata to Analyse Poverty and the Impact of Policy Change among Older Australians," Economic Papers, The Economic Society of Australia, vol. 28(2), pages 102-120, 06.
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