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Semiparametric small area estimation for binary outcomes with application to unemployment estimation for local authorities in the UK

Journal Article


Abstract


  • A new semiparametric and robust approach to small area estimation for discrete outcomes is proposed. The methodology represents an efficient and easily computed alternative to prediction by using a generalized linear mixed model and is based on an extension of M-quantile regression. In addition, two estimators of the prediction mean-squared error are described: one based on Taylor linearization and another based on the block bootstrap. The methodology proposed is applied to UK annual Labour Force Survey data for estimating the proportion of the unemployed in local authorities in the UK. The properties of estimators are further empirically assessed in model-based simulations.

Authors


  •   Chambers, Raymond L.
  •   Salvati, Nicola (external author)
  •   Tzavidis, Nikos (external author)

Publication Date


  • 2016

Citation


  • Chambers, R., Salvati, N. & Tzavidis, N. (2016). Semiparametric small area estimation for binary outcomes with application to unemployment estimation for local authorities in the UK. Journal of the Royal Statistical Society. Series A: Statistics in Society, 179 (2), 453-479.

Scopus Eid


  • 2-s2.0-84955209856

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/5287

Number Of Pages


  • 26

Start Page


  • 453

End Page


  • 479

Volume


  • 179

Issue


  • 2

Abstract


  • A new semiparametric and robust approach to small area estimation for discrete outcomes is proposed. The methodology represents an efficient and easily computed alternative to prediction by using a generalized linear mixed model and is based on an extension of M-quantile regression. In addition, two estimators of the prediction mean-squared error are described: one based on Taylor linearization and another based on the block bootstrap. The methodology proposed is applied to UK annual Labour Force Survey data for estimating the proportion of the unemployed in local authorities in the UK. The properties of estimators are further empirically assessed in model-based simulations.

Authors


  •   Chambers, Raymond L.
  •   Salvati, Nicola (external author)
  •   Tzavidis, Nikos (external author)

Publication Date


  • 2016

Citation


  • Chambers, R., Salvati, N. & Tzavidis, N. (2016). Semiparametric small area estimation for binary outcomes with application to unemployment estimation for local authorities in the UK. Journal of the Royal Statistical Society. Series A: Statistics in Society, 179 (2), 453-479.

Scopus Eid


  • 2-s2.0-84955209856

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/5287

Number Of Pages


  • 26

Start Page


  • 453

End Page


  • 479

Volume


  • 179

Issue


  • 2