Presenter: Dr. Carolina Franco, Principal Statistician, NORC at the University of Chicago, USA
Small area estimation (SAE) techniques can lead to greatly improved estimates relative to direct survey estimates when there is a large number of domains of interest and a limited overall sample size, which is often the case in surveys. When successfully applied, SAE can dramatically reduce measures of uncertainty and provide estimates for domains with no survey data. It can allow for publishing of official estimates at lower levels of aggregation. We will discuss the following topics: What is small area estimation (SAE)? What are the potential benefits of SAE? Examples of real applications of small area estimation; An introduction to area-level and unit-level models;; Discussion of frequently used software.