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Statistical Inference in Micro Simulation Models: Incorporating external information

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  • Klevmarken, N. Anders

    ()
    (Department of Economics)

Abstract

In practical applications of micro simulation models very little is usually known about the properties of the simulated values. This paper argues that we need to apply the same rigorous standards for inference in micro simulation work as in scientific work generally. If not, then micro simulation models will loose in credibility. The paper first discusses how the structure of the model will determine inference and then follow sections on estimation and validation. Differences between inference in static and dynamic models are noted and then the paper focuses on the estimation of behavioral parameters. There are three themes: calibration viewed as estimation subject to external constraints, piece meal vs. system-wide estimation, and simulation based estimation.

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Bibliographic Info

Paper provided by Uppsala University, Department of Economics in its series Working Paper Series with number 1998:20.

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Length: 25 pages
Date of creation: 01 Oct 1998
Date of revision:
Publication status: Published in Mathematics and Computers in Simulation, 2002, pages 255-265.
Handle: RePEc:hhs:uunewp:1998_020

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Postal: Department of Economics, Uppsala University, P. O. Box 513, SE-751 20 Uppsala, Sweden
Phone: + 46 18 471 25 00
Fax: + 46 18 471 14 78
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Web page: http://www.nek.uu.se/
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Related research

Keywords: Micro simulation; Alignment; Calibration; System-wide estimation; Simulation-based estimation;

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Cited by:
  1. Zucchelli, E & Jones, A.M & Rice, N, 2010. "The evaluation of health policies through microsimulation methods," Health, Econometrics and Data Group (HEDG) Working Papers 10/03, HEDG, c/o Department of Economics, University of York.
  2. Zamac, Jovan & Hallberg, Daniel & Lindh, Thomas, 2008. "Low fertility and long run growth in an economy with a large public sector," CAFO Working Papers 2009:5, Centre for Labour Market Policy Research (CAFO), School of Business and Economics, Linnaeus University.
  3. Jinjing Li & Cathal O'Donoghue, 2013. "A survey of dynamic microsimulation models: uses, model structure and methodology," International Journal of Microsimulation, Interational Microsimulation Association, vol. 6(2), pages 3-55.
  4. Michal Myck & Mateusz Najsztub, 2014. "Data and Model Cross-Validation to Improve Accuracy of Microsimulation Results: Estimates for the Polish Household Budget Survey," Discussion Papers of DIW Berlin 1368, DIW Berlin, German Institute for Economic Research.
  5. 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.
  6. John Creedy, 2003. "Survey Reweighting for Tax Microsimulation Modelling," Treasury Working Paper Series 03/17, New Zealand Treasury.
  7. Pasquale Cirillo & Carlo Bianchi & Mauro Gallegati & Pietro Vagliasindi, 2006. "Validating and Calibrating Agent-based Models: a Case Study," Computing in Economics and Finance 2006 277, Society for Computational Economics.
  8. Eugenio Zucchelli & Andrew M Jones & Nigel Rice, 2012. "The evaluation of health policies through dynamic microsimulation methods," International Journal of Microsimulation, Interational Microsimulation Association, vol. 5(1), pages 2-20.
  9. Pasquale Cirillo & Mauro Gallegati, 2012. "The Empirical Validation of an Agent-based Model," Eastern Economic Journal, Palgrave Macmillan, vol. 38(4), pages 525-547.

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