Selection model for domains across time: application to labour force survey by economic activities

María José Lombardía (), Esther López-Vizcaíno and Cristina Rueda
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María José Lombardía: Universidade da Coruña
Esther López-Vizcaíno: Instituto Galego de Estatística
Cristina Rueda: Universidad de Valladolid

TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, 2021, vol. 30, issue 1, No 14, 228-254

Abstract: Abstract This paper introduces a small area estimation approach that borrows strength across domains (areas) and time and is efficiently used to obtain labour force estimators by economic activity. Specifically, the data across time are used to select different models for each domain; such selection is done with an aggregated mixed generalized Akaike information criterion statistic which is obtained using data across all time points and then is split into individual component for each domain. The approach makes a selection from different estimators, including the direct estimator, synthetic and mixed estimators derived from different models using auxiliary information. Results from several simulation experiments, some with original designs, show the good performance of the approach against standard small area approaches. In addition, it is shown the important practical advantages in the real application.

Keywords: Akaike information criterion; Bootstrap; Fay–Herriot model; Generalized degrees of freedom; Monotone model; Small area estimation; Spline regression; 62; 62J05; 62P20; 62G08 (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s11749-020-00712-4

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