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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

    as
    1. Robert Tanton & Paul Williamson & Ann Harding, 2014. "Comparing Two Methods of Reweighting a Survey File to Small Area Data," International Journal of Microsimulation, International Microsimulation Association, vol. 7(1), pages 76-99.
    2. M Birkin & M Clarke, 1988. "SYNTHESIS -- a synthetic spatial information system for urban and regional analysis: methods and examples," Environment and Planning A, Pion Ltd, London, vol. 20(12), pages 1645-1671, December.
    3. Merz, Joachim, 1991. "Microsimulation -- A survey of principles, developments and applications," International Journal of Forecasting, Elsevier, vol. 7(1), pages 77-104, May.
    4. Rodgers, Willard L, 1984. "An Evaluation of Statistical Matching," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(1), pages 91-102, January.
    5. Moriarity, Chris & Scheuren, Fritz, 2003. "A Note on Rubin's Statistical Matching Using File Concatenation with Adjusted Weights and Multiple Imputations," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(1), pages 65-73, January.
    6. Harding, Ann & Lloyd, Rachel & Bill, Anthea & King, Anthony, 2004. "Assessing Poverty and Inequality at a Detailed Regional Level: New Advances in Spatial Microsimulation," WIDER Working Paper Series 026, 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.
    8. Mark Tranmer & Andrew Pickles & Ed Fieldhouse & Mark Elliot & Angela Dale & Mark Brown & David Martin & David Steel & Chris Gardiner, 2005. "The case for small area microdata," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 168(1), pages 29-49.
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    1. repec:ijm:journl:v10:y:2017:i:1:p:167-200 is not listed on IDEAS
    2. repec:ijm:journl:v109:y:2017:i:1:p:167-200 is not listed on IDEAS
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. repec:arp:ajlsar:2017:p:82-88 is not listed on IDEAS

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