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Expectile regression for multicategory outcomes with application to small area estimation of labour force participation

Expectile regression for multicategory outcomes with application to small area estimation of labour force participation
Expectile regression for multicategory outcomes with application to small area estimation of labour force participation
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 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 𝑀-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 multicategory outcomes.
0964-1998
Dawber, James
85c7c036-2ae3-4c57-a8b3-9f5223cd4da6
Salvati, Nicola
9be298e5-de55-4a24-9361-054a2ec09726
Fabrizi, Enrico
b8cbe145-06b2-4756-b169-393a4905521d
Tzavidis, Nikos
431ec55d-c147-466d-9c65-0f377b0c1f6a
Dawber, James
85c7c036-2ae3-4c57-a8b3-9f5223cd4da6
Salvati, Nicola
9be298e5-de55-4a24-9361-054a2ec09726
Fabrizi, Enrico
b8cbe145-06b2-4756-b169-393a4905521d
Tzavidis, Nikos
431ec55d-c147-466d-9c65-0f377b0c1f6a

Dawber, James, Salvati, Nicola, Fabrizi, Enrico and Tzavidis, Nikos (2022) Expectile regression for multicategory outcomes with application to small area estimation of labour force participation. Journal of the Royal Statistical Society: Series A (Statistics in Society). (doi:10.1111/rssa.12953). (In Press)

Record type: Article

Abstract

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 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 𝑀-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 multicategory outcomes.

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multinomial-expectiles-paper-JRSS-A-final-submission - Accepted Manuscript
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Accepted/In Press date: 22 August 2022

Identifiers

Local EPrints ID: 470093
URI: http://eprints.soton.ac.uk/id/eprint/470093
ISSN: 0964-1998
PURE UUID: 47730aa2-6fe6-428d-91f4-e6edca3b8b32
ORCID for Nikos Tzavidis: ORCID iD orcid.org/0000-0002-8413-8095

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Date deposited: 03 Oct 2022 16:48
Last modified: 04 Oct 2022 01:39

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Contributors

Author: James Dawber
Author: Nicola Salvati
Author: Enrico Fabrizi
Author: Nikos Tzavidis ORCID iD

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