IDEAS home Printed from https://ideas.repec.org/p/fth/uppaal/199820.html
   My bibliography  Save this paper

Statistical Inference in Micro Simulation Models: Incorporationg External Information

Author

Listed:
  • Klevmarken, N.A.

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 rigourous standards for inference in micro simulation work as in scientific worl generally. If not, then micro simulation models will loose in credibility.

Suggested Citation

  • Klevmarken, N.A., 1998. "Statistical Inference in Micro Simulation Models: Incorporationg External Information," Papers 1998:20, Uppsala - Working Paper Series.
  • Handle: RePEc:fth:uppaal:1998:20
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Merz, Joachim, 1994. "Microdata Adjustment by the Minimum Information Loss Principle," MPRA Paper 7231, University Library of Munich, Germany.
    2. Anders Klevmarken, 2022. "Statistical Inference in Micro-simulation Models: Incorporating External Information," International Journal of Microsimulation, International Microsimulation Association, vol. 15(1), pages 111-120.
    3. N. Anders Klevmarken, 1997. "Behavioral Modeling in Micro Simulation Models. A Survey," Working Paper Series 1997:31, Uppsala University, Department of Economics.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Matteo Richiardi & Ross E. Richardson, 2017. "JAS-mine: A new platform for microsimulation and agent-based modelling," International Journal of Microsimulation, International Microsimulation Association, vol. 10(1), pages 106-134.
    2. Michal Myck & Mateusz Najsztub, 2015. "Data and Model Cross-validation to Improve Accuracy of Microsimulation Results: Estimates for the Polish Household Budget Survey," International Journal of Microsimulation, International Microsimulation Association, vol. 8(1), pages 33-66.
    3. Tobias Schoch & André Müller, 2020. "Treatment of sample under-representation and skewed heavy-tailed distributions in survey-based microsimulation: An analysis of redistribution effects in compulsory health care insurance in Switzerland," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 14(3), pages 267-304, December.
    4. Bianchi, Carlo & Cirillo, Pasquale & Gallegati, Mauro & Vagliasindi, Pietro A., 2008. "Validation in agent-based models: An investigation on the CATS model," Journal of Economic Behavior & Organization, Elsevier, vol. 67(3-4), pages 947-964, September.
    5. Carlo Bianchi & Pasquale Cirillo & Mauro Gallegati & Pietro Vagliasindi, 2007. "Validating and Calibrating Agent-Based Models: A Case Study," Computational Economics, Springer;Society for Computational Economics, vol. 30(3), pages 245-264, October.
    6. Eugenio Zucchelli & Andrew M Jones & Nigel Rice, 2012. "The evaluation of health policies through dynamic microsimulation methods," International Journal of Microsimulation, International Microsimulation Association, vol. 5(1), pages 2-20.
    7. Vincent Touzé & Cyrille Hagneré & Gaël Dupont, 2003. "Les modèles de microsimulation dynamique dans l’analyse des réformes des systèmes de retraites : une tentative de bilan," Économie et Prévision, Programme National Persée, vol. 160(4), pages 167-191.
    8. Anders Klevmarken, 2022. "Statistical Inference in Micro-simulation Models: Incorporating External Information," International Journal of Microsimulation, International Microsimulation Association, vol. 15(1), pages 111-120.
    9. Jovan Žamac & Daniel Hallberg & Thomas Lindh, 2010. "Low Fertility and Long-Run Growth in an Economy with a Large Public Sector [Fécondité basse et croissance à long terme dans une économie à secteur public très développé]," European Journal of Population, Springer;European Association for Population Studies, vol. 26(2), pages 183-205, May.
    10. 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.
    11. Verbist, Gerlinde & Goedemé, Tim & Van den Bosch, Karel & Salanauskaite, Lina, 2013. "Testing the statistical significance of microsimulation results: often easier than you think. A technical note," EUROMOD Working Papers EM18/13, EUROMOD at the Institute for Social and Economic Research.
    12. Jinjing Li & Cathal O'Donoghue, 2013. "A survey of dynamic microsimulation models: uses, model structure and methodology," International Journal of Microsimulation, International Microsimulation Association, vol. 6(2), pages 3-55.
    13. John Creedy & Ivan Tuckwell, 2004. "Reweighting Household Surveys for Tax Microsimulation Modelling: An Application to the New Zealand Household Economic Survey," Australian Journal of Labour Economics (AJLE), Bankwest Curtin Economics Centre (BCEC), Curtin Business School, vol. 7(1), pages 71-88, March.
    14. Elisa Baroni & Matteo Richiardi, 2007. "Orcutt’s Vision, 50 years on," LABORatorio R. Revelli Working Papers Series 65, LABORatorio R. Revelli, Centre for Employment Studies.
    15. Pasquale Cirillo & Mauro Gallegati, 2012. "The Empirical Validation of an Agent-based Model," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 38(4), pages 525-547.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Eugenio Zucchelli & Andrew M Jones & Nigel Rice, 2012. "The evaluation of health policies through dynamic microsimulation methods," International Journal of Microsimulation, International Microsimulation Association, vol. 5(1), pages 2-20.
    2. Gaël Dupont & Cyrille Hagneré & Vincent Touzé, 2003. "Les modèles de microsimulation dynamique dans l'analyse des réformes des systèmes de retraites : une tentative de bilan," Economie & Prévision, La Documentation Française, vol. 0(4), pages 167-191.
    3. Jinjing Li & Cathal O'Donoghue, 2013. "A survey of dynamic microsimulation models: uses, model structure and methodology," International Journal of Microsimulation, International Microsimulation Association, vol. 6(2), pages 3-55.
    4. 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.
    5. Terance J. Rephann & Kalle Mäkilä & Einar Holm, 2005. "Microsimulation for Local Impact Analysis: an Application to Plant Shutdown," Journal of Regional Science, Wiley Blackwell, vol. 45(1), pages 183-222, February.
    6. repec:hal:wpspec:info:hdl:2441/f4rshpf3v1umfa09lat214kj4 is not listed on IDEAS
    7. 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.
    8. Matteo Richiardi & Ross E. Richardson, 2017. "JAS-mine: A new platform for microsimulation and agent-based modelling," International Journal of Microsimulation, International Microsimulation Association, vol. 10(1), pages 106-134.
    9. Martin Spielauer & René Houle, 2004. "Sample size and statistical significance of hazard regression parameters. An exploration by means of Monte Carlo simulation of four transition models based on Hungarian GGS data," MPIDR Working Papers WP-2004-020, Max Planck Institute for Demographic Research, Rostock, Germany.
    10. Agenor, Pierre-Richard & Chen, Derek H.C. & Grimm, Michael, 2004. "Linking representative household models with household surveys for poverty analysis : a comparison of alternative methodologies," Policy Research Working Paper Series 3343, The World Bank.
    11. Nicolas Hérault, 2009. "Les apports de la micro-simulation aux modèles d'équilibre général : application au cas de l'Afrique du Sud," Economie & Prévision, La Documentation Française, vol. 0(1), pages 123-135.
    12. Jovan Žamac & Daniel Hallberg & Thomas Lindh, 2010. "Low Fertility and Long-Run Growth in an Economy with a Large Public Sector [Fécondité basse et croissance à long terme dans une économie à secteur public très développé]," European Journal of Population, Springer;European Association for Population Studies, vol. 26(2), pages 183-205, May.
    13. Gijs Dekkers, 2015. "The simulation properties of microsimulation models with static and dynamic ageing a brief guide into choosing one type of model over the other," International Journal of Microsimulation, International Microsimulation Association, vol. 8(1), pages 97-109.
    14. repec:spo:wpecon:info:hdl:2441/f4rshpf3v1umfa09lat214kj4 is not listed on IDEAS
    15. Grösche, Peter & Schröder, Carsten, 2011. "Eliciting public support for greening the electricity mix using random parameter techniques," Energy Economics, Elsevier, vol. 33(2), pages 363-370, March.
    16. Pasquale Cirillo & Mauro Gallegati, 2012. "The Empirical Validation of an Agent-based Model," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 38(4), pages 525-547.
    17. Tilmann Rave & Ursula Triebswetter, 2006. "Economic impacts of environmental regulations," ifo Forschungsberichte, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 30.
    18. repec:hal:spmain:info:hdl:2441/f4rshpf3v1umfa09lat214kj4 is not listed on IDEAS
    19. Nicola Branson, 2009. "Re-weighting the OHS and LFS National household Survey Data to create a consistent series over time: A Cross Entropy Estimation Approach," SALDRU Working Papers 38, Southern Africa Labour and Development Research Unit, University of Cape Town.
    20. Joachim Merz & Dominik Hanglberger & Rafael Rucha, 2009. "The Timing of Daily Demand for Goods and Services – Multivariate Probit Estimates and Microsimulation Results for an Aged Population with German Time Use Diary Data," FFB-Discussionpaper 77, Research Institute on Professions (Forschungsinstitut Freie Berufe (FFB)), LEUPHANA University Lüneburg.
    21. Merz, Joachim, 1995. "MICSIM : Concept, Developments and Applications of a PC-Microsimulation Model for Research and Teaching," MPRA Paper 16029, University Library of Munich, Germany.
    22. Tobias Schoch & André Müller, 2020. "Treatment of sample under-representation and skewed heavy-tailed distributions in survey-based microsimulation: An analysis of redistribution effects in compulsory health care insurance in Switzerland," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 14(3), pages 267-304, December.
    23. Joachim Merz & Rainer Lang, 1997. "Preferred vs. Actual Working Hours - A Ten Years Paneleconometric Analysis for Professions, Entrepreneurs and Employees in Germany," FFB-Discussionpaper 23, Research Institute on Professions (Forschungsinstitut Freie Berufe (FFB)), LEUPHANA University Lüneburg.

    More about this item

    Keywords

    ECONOMIC MODELS ; SIMULATION;

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:fth:uppaal:1998:20. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Thomas Krichel (email available below). General contact details of provider: https://edirc.repec.org/data/nekuuse.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.