1. Hierarchical Bayes multivariate estimation of poverty rates based on increasing thresholds for small domains
- Author
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Maria Rosaria Ferrante, Enrico Fabrizi, Carlo Trivisano, Silvia Pacei, E. Fabrizi, M.R. Ferrante, S. Pacei, and C. Trivisano
- Subjects
Statistics and Probability ,Multivariate statistics ,Settore SECS-S/03 - STATISTICA ECONOMICA ,Applied Mathematics ,Hierarchical Bayes modeling ,Bayesian probability ,Posterior probability ,Estimator ,Fay Herriot model ,Sample (statistics) ,Markov chain Monte Carlo ,Beta distribution ,Computational Mathematics ,Bayes' theorem ,symbols.namesake ,Computational Theory and Mathematics ,Statistics ,Econometrics ,symbols ,FAY-HERRIOT MODEL ,Mathematics - Abstract
A model-based small area method for calculating estimates of poverty rates based on different thresholds for subsets of the Italian population is proposed. The subsets are obtained by cross-classifying by household type and administrative region. The suggested estimators satisfy the following coherence properties: (i) within a given area, rates associated with increasing thresholds are monotonically increasing; (ii) interval estimators have lower and upper bounds within the interval (0, 1); (iii) when a large domain-specific sample is available the small area estimate is close to the one obtained using standard design-based methods; (iv) estimates of poverty rates should also be produced for domains for which there is no sample or when no poor households are included in the sample. A hierarchical Bayesian approach to estimation is adopted. Posterior distributions are approximated by means of MCMC computation methods. Empirical analysis is based on data from the 2005 wave of the EU-SILC survey.
- Published
- 2011