Simulating the characteristics of populations at the small area level: New validation techniques for a spatial microsimulation model in Australia
These days spatial microsimulation modelling plays a vital role in policy analysis for small areas. Most developed countries are using these tools in ways to make knowledgeable decisions on major policy issues at local levels. However, building an appropriate model is very difficult for many reasons. For example, the creation of reliable spatial microdata is still challenging. In addition there has not been much research on testing statistical significance of the model outputs yet, and deriving estimates of how reliable these outputs may be. This paper deals with the spatial microsimulation model building procedure for simulating synthetic spatial microdata, and then estimating small area housing stress in Australia. Geographic maps for small area housing stress estimates are illustrated. The research also demonstrates a new system to test the statistical significance of the model estimates.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Volume (Year): 57 (2013)
Issue (Month): 1 ()
|Contact details of provider:|| Web page: http://www.elsevier.com/locate/csda|
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Stephen Hynes & Karyn Morrissey & Cathal O’Donoghue, 2005.
"Building a Static Farm Level Spatial Microsimulation Model: Statistically Matching the Irish National Farm Survey to the Irish Census of Agriculture,"
0506, Rural Economy and Development Programme,Teagasc.
- Stephen Hynes & Karyn Morrissey & Cathal O'donoghue, 2006. "Building a Static Farm Level Spatial Microsimulation Model: Statistically Matching the Irish National Farm Survey to the Irish Census of Agriculture," ERSA conference papers ersa06p431, European Regional Science Association.
- 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.
- 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, 06.
- Edwards, Kimberley L. & Clarke, Graham P., 2009. "The design and validation of a spatial microsimulation model of obesogenic environments for children in Leeds, UK: SimObesity," Social Science & Medicine, Elsevier, vol. 69(7), pages 1127-1134, October.
- 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.
- Robert Tanton & Yogi Vidyattama, 2010. "Pushing It To The Edge: Extending Generalised Regression As A Spatial Microsimulation Method," International Journal of Microsimulation, International Microsimulation Association, vol. 3(2), pages 23-33.
When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:57:y:2013:i:1:p:149-165. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Shamier, Wendy)
If references are entirely missing, you can add them using this form.