Hierarchical Bayesian models for the estimation of unemployment rates in small domains of the italian labour force survey
AbstractIn this paper we introduce shrinkage estimators for the estimation of the Unemployment rate in small domains of the Italian Labour Force Survey. The proposed estimators are based on Hierarchical Linear Mixed Models and on the borrowing strength on both time series and cross section. Auxiliary information from source external to the Labour Force Survey is not considered. A Hierarchical Bayesian approach is adopted, in which models are solved by means of MCMC sampling algorithms. This allows to measure variability associated to estimators accounting, in a simple way, for all e uncertainty sources. Results highlight how, simple hierarchical models allows for remarkable gain in efficiency with respect to published estimates, and that models with a time series component perform better than those based exclusively on data from the same repetition of the survey.
How to Cite
Fabrizi, E. (2002). Hierarchical Bayesian models for the estimation of unemployment rates in small domains of the italian labour force survey. Statistica, 62(4), 603–618. https://doi.org/10.6092/issn.1973-2201/429
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