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Evaluating Binary Alignment Methods in Microsimulation Models

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Abstract
Alignment is a widely adopted technique in the field of microsimulation for social and economic policy research. However, limited research has been devoted to understanding the statistical properties of the various alignment algorithms currently in use. This paper discusses and evaluates six common alignment algorithms used in the dynamic microsimulation through a set of theoretical and statistical criteria proposed in the earlier literature (e.g. Morrison 2006; O’Donoghue 2010). This paper presents and compares the alignment processes, probability transformations, and the statistical properties of alignment outputs in transparent and controlled setups. The results suggest that there is no single best method for all simulation scenarios. Instead, the choice of alignment method might need to be adapted to the assumptions and requirements in a specific project.

Suggested Citation

  • Jinjing Li & Cathal O'Donoghue, 2014. "Evaluating Binary Alignment Methods in Microsimulation Models," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 17(1), pages 1-15.
    • Handle: RePEc:jas:jasssj:2013-16-3
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    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. Johannes Geyer & Salmai Qari & Hermann Buslei & Peter Haan, 2021. "DySiMo Dokumentation: Version 1.0," Data Documentation 101, DIW Berlin, German Institute for Economic Research.
    3. Cathal O'Donoghue & Denisa M. Sologon & Iryna Kyzyma & John McHale, 2020. "Modelling the Distributional Impact of the COVID‐19 Crisis," Fiscal Studies, John Wiley & Sons, vol. 41(2), pages 321-336, June.
    4. Barry J. Milne & Roy Lay-Yee & Jessica M. Mc Lay & Janet Pearson & Martin von Randow & Peter Davis, 2015. "Modelling the Early life-course (MELC): A Microsimulation Model of Child Development in New Zealand," International Journal of Microsimulation, International Microsimulation Association, vol. 8(2), pages 28-60.
    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. Gijs Dekkers & Richard Cumpston, 2012. "On weights in dynamic-ageing microsimulation models," International Journal of Microsimulation, International Microsimulation Association, vol. 5(2), pages 59-65.
    7. O'Donoghue, Cathal & Sologon, Denisa Maria, 2023. "The Transformation of Public Policy Analysis in Times of Crisis – A Microsimulation-Nowcasting Method Using Big Data," IZA Discussion Papers 15937, Institute of Labor Economics (IZA).
    8. Jinjing Li & Yogi Vidyattama & Hai Anh La & Riyana Miranti & Denisa M. Sologon, 2022. "Estimating the Impact of Covid-19 and Policy Responses on Australian Income Distribution Using Incomplete Data," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 162(1), pages 1-31, July.
    9. Mohana Mondal & Michael P. Cameron & Jacques Poot, 2021. "Towards a dynamic spatial microsimulation model for projecting Auckland's spatial distribution of ethnic groups," Working Papers in Economics 21/12, University of Waikato.
    10. H. Xavier Jara & Lourdes Montesdeoca & Iva Tasseva, 2022. "The Role of Automatic Stabilizers and Emergency Tax–Benefit Policies During the COVID-19 Pandemic: Evidence from Ecuador," The European Journal of Development Research, Palgrave Macmillan;European Association of Development Research and Training Institutes (EADI), vol. 34(6), pages 2787-2809, December.
    11. O'Donoghue, Cathal & M. Sologon, Denisa & Kyzyma, Iryna & McHale, John, 2020. "Modelling the distributional impact of the Covid-19 crisis in Ireland," Centre for Microsimulation and Policy Analysis Working Paper Series CEMPA4/20, Centre for Microsimulation and Policy Analysis at the Institute for Social and Economic Research.
    12. Geyer, Johannes, 2021. "Die Folgen der Corona-Krise für die Anwartschaften an die gesetzliche Rentenversicherung," Working Paper Forschungsförderung 216, Hans-Böckler-Stiftung, Düsseldorf.
    13. 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, Polish Statistical Association, vol. 21(4), pages 191-211, August.
    14. 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).
    15. Matteo Richiardi, 2016. "Editorial," International Journal of Microsimulation, International Microsimulation Association, vol. 9(3), pages 1-4.
    16. Magalasi, Chimwemwe, 2021. "The short-term distributional impact of COVID-19 in Malawi," EUROMOD Working Papers EM7/21, EUROMOD at the Institute for Social and Economic Research.
    17. 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.
    18. Denisa M. Sologon & Cathal O’Donoghue & Iryna Kyzyma & Jinjing Li & Jules Linden & Raymond Wagener, 2022. "The COVID-19 resilience of a continental welfare regime - nowcasting the distributional impact of the crisis," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 20(4), pages 777-809, December.
    19. repec:ijm:journl:v109:y:2017:i:1:p:106-134 is not listed on IDEAS
    20. 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.
    21. Luis Huesca & Linda Llamas & H. Xavier Jara & César O. Vargas Téllez & David Rodríguez, 2021. "The impact of the COVID-19 pandemic on poverty and inequality in Mexico," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 16(3), pages 1-19, Julio - S.
    22. 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.

    More about this item

    Keywords

    Alignment; Microsimulation; Dynamic Microsimulation; Algorithm Evaluation;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General

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