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Mate Matching for Microsimulation Models: Technical Paper 2002-3

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  • Kevin Perese

Abstract

In dynamic microsimulation models, the process of determining who marries whom in the annual marriage market has important implications for numerous other processes modeled. There are two broad methodologies used in most microsimulation models to match individuals with each other–a stable marriage algorithm approach or a stochastic framework approach. This paper reviews those two approaches and summarizes the mate-matching techniques used in other dynamic microsimulation models. Building upon the methods used in other models, this paper presents an extension to the

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  • Kevin Perese, 2002. "Mate Matching for Microsimulation Models: Technical Paper 2002-3," Working Papers 14211, Congressional Budget Office.
    • Handle: RePEc:cbo:wpaper:14211
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    1. Zhenchao Qian, 1998. "Changes in assortative mating: The impact of age and education, 1970–1890," Demography, Springer;Population Association of America (PAA), vol. 35(3), pages 279-292, August.
    2. Pencavel, John, 1998. "Assortative Mating by Schooling and the Work Behavior of Wives and Husbands," American Economic Review, American Economic Association, vol. 88(2), pages 326-329, May.
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    Cited by:

    1. Sabine Zinn, 2012. "A Mate-Matching Algorithm for Continuous-Time Microsimulation Models," International Journal of Microsimulation, International Microsimulation Association, vol. 5(1), pages 31-51.
    2. Bourguignon, François & Bussolo, Maurizio, 2013. "Income Distribution in Computable General Equilibrium Modeling," Handbook of Computable General Equilibrium Modeling, in: Peter B. Dixon & Dale Jorgenson (ed.), Handbook of Computable General Equilibrium Modeling, edition 1, volume 1, chapter 0, pages 1383-1437, Elsevier.
    3. Jan-Maarten van Sonsbeek & j.m.van.sonsbeek@vu.nl, 2011. "Micro simulations on the effects of ageing-related policy measures: The Social Affairs Department of the Netherlands Ageing and Pensions Model," International Journal of Microsimulation, International Microsimulation Association, vol. 4(1), pages 72-99.
    4. Johannes Geyer & Salmai Qari & Hermann Buslei & Peter Haan, 2021. "DySiMo Dokumentation: Version 1.0," Data Documentation 101, DIW Berlin, German Institute for Economic Research.
    5. Amy Rehder Harris & John Sabelhaus & Almudena Sevilla-Sanz, 2005. "Behavioral Effects of Social Security Reform in a Dynamic Micro-Simulation with Life-Cycle Agents: Working Paper 2005-06," Working Papers 16494, Congressional Budget Office.
    6. Josh O’Harra & John Sabelhaus & Michael Simpson, 2004. "Overview of the Congressional Budget Office Long-Term (CBOLT) Policy Simulation Model: Technical Paper 2004-01," Working Papers 15188, Congressional Budget Office.
    7. Lyndon Walker & Peter Davis, 2013. "Modelling "Marriage Markets": A Population-Scale Implementation and Parameter Test," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 16(1), pages 1-6.
    8. Jonas Zangenberg Hansen & Peter Stephensen & Joachim Borg Kristensen, 2013. "Household Formation and Housing Demand Forecasts," DREAM Working Paper Series 201308, Danish Rational Economic Agents Model, DREAM.
    9. Amy Rehder Harris & John Sabelhaus, 2005. "How Does Differential Mortality Affect Social Security Finances and Progressivity? Working Paper 2005-05," Working Papers 16493, Congressional Budget Office.

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