Methodological issues in spatial microsimulation modelling for small area estimation

Azizur Rahman, Ann Harding, Robert Tanton, Shuangzhe Liu

Research output: Contribution to journalArticlepeer-review

45 Downloads (Pure)


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
Original languageEnglish
Pages (from-to)3-22
Number of pages20
JournalInternational Journal of Microsimulation
Issue number2
Publication statusPublished - 2010


Dive into the research topics of 'Methodological issues in spatial microsimulation modelling for small area estimation'. Together they form a unique fingerprint.

Cite this