The fayherriot command for estimating small-area indicators

Christoph Halbmeier (), Ann-Kristin Kreutzmann (), Timo Schmid () and Carsten Schröder
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Christoph Halbmeier: Freie Universität Berlin
Ann-Kristin Kreutzmann: Freie Universität Berlin
Timo Schmid: Freie Universität Berlin

Stata Journal, 2019, vol. 19, issue 3, 626-644

Abstract: We introduce a command, fayherriot, that implements the Fay–Herriot model (Fay and Herriot, 1979, Journal of the American Statistical Association 74: 269–277), which is a small-area estimation technique (Rao and Molina, 2015, Small Area Estimation), in Stata. The Fay–Herriot model improves the precision of area-level direct estimates using area-level covariates. It belongs to the class of linear mixed models with normally distributed error terms. The fayherriot command encompasses options to a) produce out-of-sample predic- tions, b) adjust nonpositive random-effects variance estimates, and c) deal with the violation of model assumptions.

Keywords: fayherriot; disaggregated indicators; small-area estimation; (log-transformed) Fay–Herriot model; empirical best linear unbiased predictor (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1177/1536867X19874238

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