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Spatial Microsimulation: Developments and Potential Future Directions

Author

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  • Robert Tanton

    (National Centre for Social and Economic Modelling, Institute for Governance and Policy Analysis, University of Canberra, Canberra, Australia.)

Abstract

This paper summarises some of the latest developments in methods to estimate and validate spatial microsimulation models. The paper also attempts to identify where the potential is for new areas of development in spatial microsimulation models, based on the author’s reading of the spatial microsimulation landscape in 2018. The methods outlined in this paper are identified as significant developments in the field, and include a number of new methods for calculating or adding indicators to a spatial microsimulation model; as well as new methods of validation and estimating confidence intervals. Potential new areas of research include further development of methods for calculating confidence intervals; work on getting spatial microsimulation into the mainstream of policy analysis; work on linking models to provide input into managing complex problems in society; and work on using big data in spatial microsimulation models.

Suggested Citation

  • Robert Tanton, 2018. "Spatial Microsimulation: Developments and Potential Future Directions," International Journal of Microsimulation, International Microsimulation Association, vol. 11(1), pages 143-161.
    • Handle: RePEc:ijm:journl:v10:y:2018:i:1:p:143-161
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    File URL: http://microsimulation.org/IJM/V11_1/IJM_11_1_4.pdf
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    References listed on IDEAS

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    1. Alex Anas & Richard Arnott & Kenneth A. Small, 1998. "Urban Spatial Structure," Journal of Economic Literature, American Economic Association, vol. 36(3), pages 1426-1464, September.
    2. 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.
    3. Alex Fenton, 2016. "Spatial microsimulation estimates of household income distributions in London boroughs, 2001 and 2011," CASE Papers /196, Centre for Analysis of Social Exclusion, LSE.
    4. 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.
    5. Robert Tanton, 2014. "A Review of Spatial Microsimulation Methods," International Journal of Microsimulation, International Microsimulation Association, vol. 7(1), pages 4-25.
    6. John Creedy & Guyonne Kalb & Hsein Kew, 2007. "Confidence Intervals For Policy Reforms In Behavioural Tax Microsimulation Modelling," Bulletin of Economic Research, Wiley Blackwell, vol. 59(1), pages 37-65, January.
    7. repec:bla:ecorec:v:85:y:2009:i:s1:p:s48-s58 is not listed on IDEAS
    8. Sharyn Lymer & Laurie Brown & Ann Harding & Mandy Yap, 2009. "Predicting the need for aged care services at the small area level: the CAREMOD spatial microsimulation model," International Journal of Microsimulation, International Microsimulation Association, vol. 2(2), pages 27-42.
    9. Robert Tanton & Yogi Vidyattama & Binod Nepal & Justine McNamara, 2011. "Small area estimation using a reweighting algorithm," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 174(4), pages 931-951, October.
    10. Nicolas Hérault, 2010. "Sequential linking of Computable General Equilibrium and microsimulation models: a comparison of behavioural and reweighting techniques," International Journal of Microsimulation, International Microsimulation Association, vol. 3(1), pages 35-42.
    11. Kate A Timmins & Kimberley L Edwards, 2016. "Validation of Spatial Microsimulation Models: a Proposal to Adopt the Bland-Altman Method," International Journal of Microsimulation, International Microsimulation Association, vol. 9(2), pages 106-122.
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    Citations

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    Cited by:

    1. Sebastian Dräger & Johannes Kopp & Ralf Münnich & Simon Schmaus, 2021. "Analyse der Grundschulversorgung in Trier mit Hilfe kleinräumiger Mikrosimulationsmodelle," Research Papers in Economics 2021-01, University of Trier, Department of Economics.
    2. Sebastian Dräger & Johannes Kopp & Ralf Münnich & Simon Schmaus, 2022. "Die zukünftige Entwicklung der Grundschulversorgung im Kontext ausgewählter Wanderungsszenarien [The future development of primary school demand in the context of selected migration scenarios]," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 16(1), pages 51-77, March.
    3. Spooner, Fiona & Abrams, Jesse F. & Morrissey, Karyn & Shaddick, Gavin & Batty, Michael & Milton, Richard & Dennett, Adam & Lomax, Nik & Malleson, Nick & Nelissen, Natalie & Coleman, Alex & Nur, Jamil, 2021. "A dynamic microsimulation model for epidemics," Social Science & Medicine, Elsevier, vol. 291(C).
    4. 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).
    5. 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.
    6. 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.

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    More about this item

    Keywords

    MODEL VALIDATION; BIG DATA; SMART CITIES; CONFIDENCE INTERVAL ESTIMATION;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models

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