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Small area estimates of labour force participation under a multinomial logit mixed model
Author(s) -
Molina Isabel,
Saei Ayoub,
José Lombardía M.
Publication year - 2007
Publication title -
journal of the royal statistical society: series a (statistics in society)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.103
H-Index - 84
eISSN - 1467-985X
pISSN - 0964-1998
DOI - 10.1111/j.1467-985x.2007.00493.x
Subject(s) - estimator , bootstrapping (finance) , multinomial logistic regression , econometrics , statistics , multinomial distribution , multinomial probit , mathematics , mixed logit , logit , estimation , linearization , mean squared error , logistic regression , economics , physics , management , nonlinear system , quantum mechanics
Summary. A new methodology is developed for estimating unemployment or employment characteristics in small areas, based on the assumption that the sample totals of unemployed and employed individuals follow a multinomial logit model with random area effects. The method is illustrated with UK labour force data aggregated by sex–age groups. For these data, the accuracy of direct estimates is poor in comparison with estimates that are derived from the multinomial logit model. Furthermore, two different estimators of the mean‐squared errors are given: an analytical approximation obtained by Taylor linearization and an estimator based on bootstrapping. A simulation study for comparison of the two estimators shows the good performance of the bootstrap estimator.