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A Review of Spatial Microsimulation Methods

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  • Robert Tanton

    () (ational Centre for Social and Economic Modelling (NATSEM),)

Abstract

This paper outlines a framework for spatial microsimulation models, gives some reasons why someone may want to use a spatial microsimulation model, describes the development of spatial microsimulation over the last 30 years, summarises the different methods currently used for spatial microsimulation, and outlines how the models can be validated. In reviewing the reasons and methods for spatial microsimulation, we conclude that spatial microsimulation provides an alternative to other small area estimation methods, providing flexibility by allowing cross-tabulations to be built, and an ability to link to other models, and derive projections. Spatial microsimulation models also allow demographic changes, like births and deaths, to be included in a dynamic microsimulation model. This also allows ‘what if’ scenarios to be modelled, for example, what would happen if the birth rate increased over time. Validation of the spatial microsimulation models shows that they are now at the stage where they can provide reliable result

Suggested Citation

  • Robert Tanton, 2014. "A Review of Spatial Microsimulation Methods," International Journal of Microsimulation, International Microsimulation Association, vol. 7(1), pages 4-25.
  • Handle: RePEc:ijm:journl:v:7:y:2014:i:1:p:4-25
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    File URL: http://microsimulation.org/IJM/V7_1/2-IJM_7_1_Tanton_.pdf
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    References listed on IDEAS

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    1. 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.
    2. Cathal O'Donoghue & John Lennon & Stephen Hynes, 2009. "The Life-Cycle Income Analysis Model (LIAM): a study of a flexible dynamic microsimulation modelling computing framework," International Journal of Microsimulation, International Microsimulation Association, vol. 2(1), pages 16-31.
    3. P Williamson & M Birkin & P H Rees, 1998. "The estimation of population microdata by using data from small area statistics and samples of anonymised records," Environment and Planning A, Pion Ltd, London, vol. 30(5), pages 785-816, May.
    4. Ben Phillips & S.F. Chin & Ann Harding, 2007. "Housing Stress Today: Estimates for Statistical Local Areas in 2005," NATSEM Working Paper Series 2006 019, University of Canberra, National Centre for Social and Economic Modelling.
    5. Yogi Vidyattama & Maheshwar Rao & Itismita Mohanty & Robert Tanton, 2014. "Modelling the impact of declining Australian terms of trade on the spatial distribution of income," International Journal of Microsimulation, International Microsimulation Association, vol. 7(1), pages 100-126.
    6. Itismita Mohanty & Robert Tanton & Yogi Vidyattama & Marcia Keegan & Robert Cummins, 2013. "‘Small area estimates of Subjective Wellbeing: Spatial Microsimulation on the Australian Unity Wellbeing Index Survey’," NATSEM Working Paper Series 13/23, University of Canberra, National Centre for Social and Economic Modelling.
    7. Robert Tanton & Yogi Vidyattama & Justine McNamara & Quoc Ngu Vu & Ann Harding, 2009. "Old, Single and Poor: Using Microsimulation and Microdata to Analyse Poverty and the Impact of Policy Change among Older Australians," Economic Papers, The Economic Society of Australia, vol. 28(2), pages 102-120, June.
    8. Ann Harding & Quoc Ngu Vu & Robert Tanton & Yogi Vidyattama, 2009. "Improving Work Incentives and Incomes for Parents: The National and Geographic Impact of Liberalising the Family Tax Benefit Income Test," The Economic Record, The Economic Society of Australia, vol. 85(s1), pages 48-58, September.
    9. Yogi Vidyattama & Rebecca Cassells & Ann Harding & Justine Mcnamara, 2013. "Rich or Poor in Retirement? A Small Area Analysis of Australian Private Superannuation Savings in 2006 Using Spatial Microsimulation," Regional Studies, Taylor & Francis Journals, vol. 47(5), pages 722-739, May.
    10. P Williamson & M Birkin & P H Rees, 1998. "The Estimation of Population Microdata by Using Data from Small Area Statistics and Samples of Anonymised Records," Environment and Planning A, , vol. 30(5), pages 785-816, May.
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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. Joachim Merz & Lars Rusch, 2015. "MICSIM-4j - A General Microsimulation Model User Guide (Version 1.1)," FFB-Discussionpaper 100, Research Institute on Professions (Forschungsinstitut Freie Berufe (FFB)), LEUPHANA University Lüneburg.
    4. 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.
    5. Cathal O'Donoghue & Karyn Morrissey & John Lennon, 2014. "Spatial Microsimulation Modelling: a Review of Applications and Methodological Choices," International Journal of Microsimulation, International Microsimulation Association, vol. 7(1), pages 26-75.
    6. 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.
    7. Luigi Cannari & Giovanni D’Alessio, 2018. "Wealth inequality in Italy: reconstruction of 1968-75 data and comparison with recent estimates," Questioni di Economia e Finanza (Occasional Papers) 428, Bank of Italy, Economic Research and International Relations Area.

    More about this item

    Keywords

    Spatial microsimulation; Small Area Estimation;

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • J11 - Labor and Demographic Economics - - Demographic Economics - - - Demographic Trends, Macroeconomic Effects, and Forecasts

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