Estimation of unemployment rates in small areas of Portugal: a best linear unbiased prediction approach versus a hierarchical Bayes approach

J.M. Mendes, P.S. Coelho, Luis N. Pereira

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The high level of unemployment is one of the major problems in most European countries nowadays. Hence, the demand for small area labor market statistics has rapidly increased over the past few years. The Labour Force Survey (LFS) conducted by the Portuguese Statistical Office is the main source of official statistics on the labour market at the macro level (e.g. NUTS2 and national level). However, the LFS was not designed to produce reliable statistics at the micro level (e.g. NUTS3, municipalities or further disaggregate level) due to small sample sizes. Consequently, traditional design-based estimators are not appropriate. A solution to this problem is to consider model-based estimators that "borrow information" from related areas or past samples by using auxiliary information. This paper reviews, under the model-based approach, Best Linear Unbiased Predictors and an estimator based on the posterior predictive distribution of a Hierarchical Bayesian model. The goal of this paper is to analyze the possibility to produce accurate unemployment rate statistics at micro level from the Portuguese LFS using these kinds of estimators. This paper discusses the advantages of using each approach and the viability of its implementation.
Original languageEnglish
Title of host publication17th European Young Statisticians Meeting
PublisherFaculdade de CIâncias e Tecnologia - Universidade Nova de Lisboa
Pages171-174
Number of pages4
Publication statusPublished - 2011

Keywords

  • Best linear unbiased prediction, Hierarchical Bayes, Small area estimation, Unemployment rate AMS

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