Estimating the Uncertainty of a Small Area Estimator Based on a Microsimulation Approach

Abstract

Spatial microsimulation encompasses a range of alternative methodological approaches for the small area estimation (SAE) of target population parameters from sample survey data down to target small areas in contexts where such data are desired but not otherwise available. Although widely used, an enduring limitation of spatial microsimulation SAE approaches is their current inability to deliver reliable measures of uncertainty—and hence confidence intervals—around the small area estimates produced. In this article, we overcome this key limitation via the development of a measure of uncertainty that takes into account both variance and bias, that is, the mean squared error. This new approach is evaluated via a simulation study and demonstrated in a practical application using European Union Statistics on Income and Living Conditions data to explore income levels across Italian municipalities. Evaluations show that the approach proposed delivers accurate estimates of uncertainty and is robust to nonnormal distributions. The approach provides a significant development to widely used spatial microsimulation SAE techniques.

Estimating Health over Space and Time: A Review of Spatial Microsimulation Applied to Public Health

“…Known authors in the field were searched individually to identify manuscripts not included in the original results. This review will also describe the different approaches to spatial microsimulation for public health outcomes and behaviours, building on a wider literature that includes previous reviews [1,4,5] and further methodological developments identified in the literature [6][7][8][9]. In particular, we will discuss the range of approaches, defined by Tanton [5] and O'Donoghue et al [10] as synthetic reconstruction and reweighting broadly, bringing in examples where this process is applied to health outcomes.…”

“…As these models are used to inform policy decisions and resource allocation, there needs to be ease of communicating the uncertainty around estimates. Whitworth and colleagues [6,8] made substantial gains in this area of research. By using outputs from multilevel regression for the constraint variables in an IPF reweighting model, they were able to provide credible intervals around the estimated data within small areas.…”