TY - JOUR
T1 - Expectile regression for multi-category outcomes with application to small area estimation of labour force participation
AU - Dawber, James
AU - Salvati, Nicola
AU - Fabrizi, Enrico
AU - Tzavidis, Nikos
PY - 2022
Y1 - 2022
N2 - In many applications of small area estimation, dichotomous or categorical outcomes are the targets of statistical inference. For example, in the analysis of labour markets, proportions of working-age people in the various labour market statuses are of interest. In this paper, in line with the recent literature, we consider a classification with more than three statuses and estimate related population parameters for 611 local labour market areas using data from the 2012 Italian Labour Force Survey, administrative registers and the 2011 Census. As for the methodology, we propose multinomial expectile regression models. These models provide a means to utilise (Formula presented.) -quantile type approaches, which have been shown to be a useful alternative to mixed model approaches when parametric assumptions on the distribution of random effects cannot be met. Via a large-scale simulation study, we show how this novel approach is much faster and provides reliable results when compared to multinomial mixed model approaches, and works for any number of categories rather than just a small number of categories as is more commonly the case with existing methods. Furthermore, the proposed approach potentially provides a framework for developing other methods for prediction with multi-category outcomes.
AB - In many applications of small area estimation, dichotomous or categorical outcomes are the targets of statistical inference. For example, in the analysis of labour markets, proportions of working-age people in the various labour market statuses are of interest. In this paper, in line with the recent literature, we consider a classification with more than three statuses and estimate related population parameters for 611 local labour market areas using data from the 2012 Italian Labour Force Survey, administrative registers and the 2011 Census. As for the methodology, we propose multinomial expectile regression models. These models provide a means to utilise (Formula presented.) -quantile type approaches, which have been shown to be a useful alternative to mixed model approaches when parametric assumptions on the distribution of random effects cannot be met. Via a large-scale simulation study, we show how this novel approach is much faster and provides reliable results when compared to multinomial mixed model approaches, and works for any number of categories rather than just a small number of categories as is more commonly the case with existing methods. Furthermore, the proposed approach potentially provides a framework for developing other methods for prediction with multi-category outcomes.
KW - categorical data analysis
KW - multinomial logistic regression
KW - M-quantile estimation
KW - categorical data analysis
KW - multinomial logistic regression
KW - M-quantile estimation
UR - http://hdl.handle.net/10807/230133
U2 - 10.1111/rssa.12953
DO - 10.1111/rssa.12953
M3 - Article
SN - 0964-1998
VL - 185
SP - S590-S619
JO - JOURNAL OF THE ROYAL STATISTICAL SOCIETY. SERIES A. STATISTICS IN SOCIETY
JF - JOURNAL OF THE ROYAL STATISTICAL SOCIETY. SERIES A. STATISTICS IN SOCIETY
ER -