M. Bugallo Porto, M. D. Esteban Lefler, T. Hobza, D. Morales, A. Pérez Martín
Small area estimation of proportions of employed, unemployed and inactive people, and of unemployment rates, is a challenge due to the binary and multivariate character of the target variables in the population units. This work introduces new predictors of small area labour force indicators based on unit-level multinomial mixed models. Concerning the model, we study the behaviour of different algorithms that calculate the maximum likelihood estimators of the model parameters. We introduce empirical best and plug-in predictors of the small area quantities of interest and we estimate their mean squared errors by parametric bootstrap. Several simulation experiments are carried out to empirically investigate the properties of these estimators and predictors. Finally, a detailed application to real data from the first Spanish Labour Force Survey of 2021 is included, where the target is to map labour force indicators by province, sex and age group.
Keywords: Small area estimation, multinomial mixed model, unit-level data, labour force survey, unemployment rate, inactivity.
Scheduled
Mixed Models II
November 10, 2023 9:30 AM
HC3: Canónigos Room 3