Large-scale sample surveys are not designed to produce reliable estimates for small population domains, e.g., geographical areas or population groups. This is because area sample sizes may be “too small” to compute reliable direct estimates. Therefore, small area estimation methods, that borrow strength information from auxiliary data e.g., the Census or administrative data, can be used to produce reliable estimates. This course covers widely adopted small area estimation methods based on the direct and model-based estimation approach and it is structured in three parts. The first part is about the introduction to the small area estimation problem and the use of direct estimators to produce small area estimates. In the second part, we introduce the unit-level approach based on the Battese, Harter and Fuller model, assuming that auxiliary information is available at unit-level. The third part is on the area-level approach, based on the Fay-Herriot model. This approach is useful when the auxiliary information is available are area-level only. The course will also focus on evaluation of the small area estimates via diagnostics tools and simulation studies. Applications will also be provided in each part of the course.

 

Practical applications and examples will be in R. We will show some common R packages available to users and applications will be based both on real data and simulation studies.

 

The full syllabus of the course including the day-to-day schedule will be published here in April.

You will find the course useful if:

By the end of the course, you will:

You need to bring a laptop to the course with R (https://cran.r-project.org/) and R Studio (https://posit.co/download/rstudio-desktop/) installed to successfully participate in this course. Both programms are free and open source.

 

You should have the ability to download packages. Internet access is provided by GESIS.

 

The following R packages (and their dependencies) should be installed on your device before the course starts: