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Logit Scaling: A General Method for Alignment in Microsimulation models

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  • Peter Stephensen

    (Danish research institute of applied economic modelling, DREAM, Copenhagen, Denmark)

Abstract

A general and easily implemented method for alignment in microsimulation models is proposed. Most existing alignment methods address the binary case and they typically put special emphasis on one of the alternatives rather than treating all alternatives in a symmetric way. In this paper we propose a general mathematical foundation of multinominal alignment, which minimizes the relative entropy in the process of aligning probabilities to given targets. The method is called Logit Scaling. The analytical solution to the alignment problem is characterized and applied in deriving various properties for the method. It is demonstrated that there exits an algorithm called Bi-Proportional Scaling that converges to the solution of the problem. This is tested against two versions of the Newton-Raphson-algoritm, and it is demonstrated that it is at least twice as fast as these methods. Finally, the method is not just computational efficient but also easy to implement.

Suggested Citation

  • Peter Stephensen, 2016. "Logit Scaling: A General Method for Alignment in Microsimulation models," International Journal of Microsimulation, International Microsimulation Association, vol. 9(3), pages 89-102.
    • Handle: RePEc:ijm:journl:v:9:y:2016:i:3:p:89-102
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    References listed on IDEAS

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    Cited by:

    1. Johannes Geyer & Salmai Qari & Hermann Buslei & Peter Haan, 2021. "DySiMo Dokumentation: Version 1.0," Data Documentation 101, DIW Berlin, German Institute for Economic Research.
    2. Burgard Jan Pablo & Dieckmann Hanna & Krause Joscha & Merkle Hariolf & Münnich Ralf & Neufang Kristina M. & Schmaus Simon, 2020. "A generic business process model for conducting microsimulation studies," Statistics in Transition New Series, Statistics Poland, vol. 21(4), pages 191-211, August.
    3. Burgard, Jan Pablo & Krause, Joscha & Schmaus, Simon, 2021. "Estimation of regional transition probabilities for spatial dynamic microsimulations from survey data lacking in regional detail," Computational Statistics & Data Analysis, Elsevier, vol. 154(C).
    4. Jan Weymeirsch & Julian Ernst & Ralf Münnich, 2024. "Model Recalibration for Regional Bias Reduction in Dynamic Microsimulations," Mathematics, MDPI, vol. 12(10), pages 1-25, May.
    5. Jan Pablo Burgard & Joscha Krause & Simon Schmaus, 2019. "Estimation of Regional Transition Probabilities for Spatial Dynamic Microsimulations from Survey Data Lacking in Regional Detail," Research Papers in Economics 2019-12, University of Trier, Department of Economics.
    6. Jan Pablo Burgard & Hanna Dieckmann & Joscha Krause & Hariolf Merkle & Ralf Münnich & Kristina M. Neufang & Simon Schmaus, 2020. "A generic business process model for conducting microsimulation studies," Statistics in Transition New Series, Polish Statistical Association, vol. 21(4), pages 191-211, August.
    7. Richiardi, Matteo & Bronka, Patryk & van de Ven, Justin, 2023. "Back to the future: Agent-based modelling and dynamic microsimulation," Centre for Microsimulation and Policy Analysis Working Paper Series CEMPA8/23, Centre for Microsimulation and Policy Analysis at the Institute for Social and Economic Research.

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    More about this item

    Keywords

    Microsimulation; Alignment; Bi-proportional scaling; Relative entropy; Kullbeck-Leibler Information Criterion; Newton-Raphson; Logit scaling;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access

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