Small area estimation through spatial microsimulation models: Some methodological issues

Small area estimation has received much attention in recent decades due to increasing demand for reliable small area estimates from both public and private sectors. Traditional direct estimation requires the domain-specific sufficiently large sample. But, in reality, domain-specific sample data are usually not large enough for all small areas (even zero for some small areas) to provide adequate statistical precision of their estimates. This makes it necessary to “borrow strength” from data on related multiple characteristics and/or auxiliary variables from other neighbouring areas through appropriate models, leading to indirect or model-based estimates. This paper describes some vital methodological issues of spatial microsimulation modelling for small area estimation, with a particular emphasis given to the reweighting techniques.

Most of the review articles in small area estimation have highlighted methodologies known as “statistical approaches” - which are based on various statistical models and theories. However another type of technique called “spatial microsimulation models” has also provided small area estimates during the last decade. These models are based on economic theory and using quite different methodologies. A thorough overview on various microsimulation models shows that spatial microsimulation models are robust and have
advantages over others. In contrast to statistical approaches, the spatial microsimulation model-based approaches can operate through different reweighting techniques such as GREGWT and combinatorial optimization. A comparison between reweighting techniques reveals that they are using quite different algorithms and that their properties also vary. However their performances are fairly similar according to the advantages of spatial
microsimulation modelling. Finally the study points out some new possibilities in the spatial microsimulation methodology.