Estimating the Uncertainty of a Small Area Estimator Based on a Microsimulation Approach
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| Title: | Estimating the Uncertainty of a Small Area Estimator Based on a Microsimulation Approach |
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| Language: | English |
| Authors: | Moretti, Angelo (ORCID |
| Source: | Sociological Methods & Research. 2023 52(4):1785-1815. |
| Availability: | SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com |
| Peer Reviewed: | Y |
| Page Count: | 31 |
| Publication Date: | 2023 |
| Document Type: | Journal Articles Reports - Evaluative |
| Descriptors: | Simulation, Geometric Concepts, Computation, Measurement, Error of Measurement, Bias, Income, Municipalities, Foreign Countries |
| Geographic Terms: | Italy |
| DOI: | 10.1177/0049124120986199 |
| ISSN: | 0049-1241 1552-8294 |
| 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. |
| Abstractor: | As Provided |
| Entry Date: | 2023 |
| Accession Number: | EJ1397535 |
| Database: | ERIC |
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| 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. |
|---|---|
| ISSN: | 0049-1241 1552-8294 |
| DOI: | 10.1177/0049124120986199 |
