Transformations for Unit-Level Small Area Models

The aim of this package is to offer new methodology for unit-level small area models under transformations and limited population auxiliary information. In addition to this new methodology, the widely used nested error regression model without transformations (see "An Error-Components Model for Prediction of County Crop Areas Using Survey and Satellite Data" by Battese, Harter and Fuller (1988) ) and its well-known uncertainty estimate (see "The estimation of the mean squared error of small-area estimators" by Prasad and Rao (1990) ) are provided. In this package, the log transformation and the data-driven log-shift transformation are provided. If a transformation is selected, an appropriate method is chosen depending on the respective input of the population data: Individual population data (see "Empirical best prediction under a nested error model with log transformation" by Molina and Martín (2018) ) but also aggregated population data (see "Estimating regional income indicators under transformations and access to limited population auxiliary information" by Würz, Schmid and Tzavidis ) can be entered. Especially under limited data access, new methodologies are provided in saeTrafo. Several options are available to assess the used model and to judge, present and export its results. For a detailed description of the package and the methods used see the corresponding vignette.