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Methodological Issues in Spatial Microsimulation Modelling for Small Area Estimation

Author

Listed:
  • Azizur Rahman

    (National Centre for Social and Economic Modelling (NATSEM), University of Canberra, ACT 2601, Australia)

  • Ann Harding

    (National Centre for Social and Economic Modelling (NATSEM), University of Canberra, ACT 2601, Australia)

  • Robert Tanton

    (National Centre for Social and Economic Modelling (NATSEM), University of Canberra, ACT 2601, Australia)

  • Shuangzhe Liu

    (National Centre for Social and Economic Modelling (NATSEM), University of Canberra, ACT 2601, Australia)

Abstract

In this paper, some vital methodological issues of spatial microsimulation modelling for small area estimation have been addressed, with a particular emphasis given to the reweighting techniques. Most of the review articles in small area estimation have highlighted methodologies based on various statistical models and theories. However, spatial microsimulation modelling is emerging as a very useful alternative means of small area estimation. Our findings demonstrate that spatial microsimulation models are robust and have advantages over other type of models used for small area estimation. The technique uses different methodologies typically based on geographic models and various economic theories. In contrast to statistical model-based approaches, the spatial microsimulation model-based approaches can operate through reweighting techniques such as GREGWT and combinatorial optimization. A comparison between reweighting techniques reveals that they are using quite different iterative algorithms and that their properties also vary. The study also points out a new method for spatial microsimulation modelling

Suggested Citation

  • Azizur Rahman & Ann Harding & Robert Tanton & Shuangzhe Liu, 2010. "Methodological Issues in Spatial Microsimulation Modelling for Small Area Estimation," International Journal of Microsimulation, International Microsimulation Association, vol. 3(2), pages 3-22.
    • Handle: RePEc:ijm:journl:v:3:y:2010:i:2:p:3-22
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    File URL: http://ima.natsem.canberra.edu.au/IJM/V3_2/Volume%203%20Issue%202/1_IJM_47%20Proof.pdf
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    References listed on IDEAS

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    6. Ann Harding & Rachel Lloyd & Anthea Bill & Anthony King, 2004. "Assessing Poverty and Inequality at a Detailed Regional Level: New Advances in Spatial Microsimulation," WIDER Working Paper Series RP2004-26, World Institute for Development Economic Research (UNU-WIDER).
    7. Dimitris Ballas & Graham Clarke & John Dewhurst, 2006. "Modelling the Socio-economic Impacts of Major Job Loss or Gain at the Local Level: a Spatial Microsimulation Framework," Spatial Economic Analysis, Taylor & Francis Journals, vol. 1(1), pages 127-146.
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    Cited by:

    1. 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.
    2. 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.
    3. Niall Farrell, 2024. "Small Area Poverty Estimation by Conditional Monte Carlo," Papers WP773, Economic and Social Research Institute (ESRI).
    4. M. Esteban Muñoz H. & Irene Peters, 2014. "Constructing an Urban Microsimulation Model to Assess the Influence of Demographics on Heat Consumption," International Journal of Microsimulation, International Microsimulation Association, vol. 7(1), pages 127-157.
    5. Miriam Hortas-Rico & Jorge Onrubia & Daniele Pacifico, 2014. "Estimating the Personal Income Distribution in Spanish Municipalities Using Tax Micro-Data," International Center for Public Policy Working Paper Series, at AYSPS, GSU paper1419, International Center for Public Policy, Andrew Young School of Policy Studies, Georgia State University.
    6. Miriam Hortas-Rico & Jorge Onrubia & Daniele Pacifico, 2013. "Personal Income Distribution at the Local Level. An Estimation for Spanish Municipalities Using Tax Microdata," International Center for Public Policy Working Paper Series, at AYSPS, GSU paper1314, International Center for Public Policy, Andrew Young School of Policy Studies, Georgia State University.
    7. Ian Philips & Graham Clarke & David Watling, 2017. "A Fine Grained Hybrid Spatial Microsimulation Technique for Generating Detailed Synthetic Individuals from Multiple Data Sources: An Application To Walking And Cycling," International Journal of Microsimulation, International Microsimulation Association, vol. 10(1), pages 167-200.
    8. repec:ijm:journl:v109:y:2017:i:1:p:167-200 is not listed on IDEAS
    9. M. Esteban Muñoz H. & Ivan Dochev & Hannes Seller & Irene Peters, 2016. "Constructing a Synthetic City for Estimating Spatially Disaggregated Heat Demand," International Journal of Microsimulation, International Microsimulation Association, vol. 9(3), pages 66-88.
    10. Alberto Vitalini & Simona Ballabio & Flavio Verrecchia, 2024. "Rebuilding a pseudo population register for estimating physical vulnerability at the local level: a case study of spatial microsimulation in Sondrio," RIEDS - Rivista Italiana di Economia, Demografia e Statistica - The Italian Journal of Economic, Demographic and Statistical Studies, SIEDS Societa' Italiana di Economia Demografia e Statistica, vol. 78(1), pages 55-64, January-M.
    11. Rahman, Azizur & Harding, Ann & Tanton, Robert & Liu, Shuangzhe, 2013. "Simulating the characteristics of populations at the small area level: New validation techniques for a spatial microsimulation model in Australia," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 149-165.
    12. 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).
    13. Yogi Vidyattama & Riyana Miranti & Justine McNamara & Robert Tanton & Ann Harding, 2013. "The Challenges of Combining Two Databases in Small-Area Estimation: An Example Using Spatial Microsimulation of Child Poverty," Environment and Planning A, , vol. 45(2), pages 344-361, February.
    14. Md. Kamruzzaman & Sumona Sharmin & Md. Abdul Hakim, 2017. "Socio-Psychology of Rapping in Criminological Nexus," Academic Journal of Life Sciences, Academic Research Publishing Group, vol. 3(11), pages 82-88, 11-2017.

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