saeTrafo {saeTrafo}

The R Package saeTrafo for Estimating unit-level Small Area Models under Transformations

Description

The package saeTrafo supports estimating regional means based on the nested error regression model (Battese et al., 1988). Therefore, point estimation and mean squared error estimation (Prasad and Rao, 1990) for the classical model is offered. In addition to the classical model, the logarithmic and the data-driven log-shift transformation are provided. The core function NER_Trafo allows several options to enter population data: Either individual population data or only aggregates can be entered. If full population data is given, the method of Molina and Martín (2018) is applied. Compared to other small area packages, these transformations are accessible in the absence of population micro-data. Only population aggregates (mean values, population sizes and preferably also covariances) need to be supplied. The methodology for point and mean squared error estimates is described in Wuerz et al. (2022) and is made available in a user-friendly way within saeTrafo.

Details

The estimation function is called NER_Trafo. For this function, several methods are available such as estimators.saeTrafo and summaries.saeTrafo. For a full list, please see saeTrafoObject. Furthermore, functions map_plot and write.excel help to visualize and export results. An overview of all currently provided functions can be requested by library(help=saeTrafo).

References

Battese, G.E., Harter, R.M. and Fuller, W.A. (1988). An Error-Components Model for Predictions of County Crop Areas Using Survey and Satellite Data. Journal of the American Statistical Association, Vol.83, No. 401, 28-36.

Molina, I. and Martín, N. (2018). Empirical best prediction under a nested error model with log transformation. The Annals of Statistics, Vol.46, No. 5, 1961–1993.

Prasad, N.N., Rao, J.N.K. (1990). The estimation of the mean squared error of small-area estimators. Journal of the American statistical association, Vol.85, No. 409, 163-171.

Wuerz, N., Schmid, T., Tzavidis, N. (2022). Estimating regional income indicators under transformations and access to limited population auxiliary information. Unpublished.