Abstract
In business surveys, estimates of means and totals for subnational regions, industries
and business classes can be too imprecise because of the small sample sizes that are
available for subpopulations.We propose a small area technique for the estimation of totals for
skewed target variables, which are typical of business data. We adopt a Bayesian approach
to inference. We specify a prior distribution for the random effects based on the idea of local
shrinkage, which is suitable when auxiliary variables with strong predictive power are available:
another feature that is often displayed by business survey data. This flexible modelling of random
effects leads to predictions in agreement with those based on global shrinkage for most of
the areas, but enables us to obtain less shrunken and thereby less biased estimates for areas
characterized by large model residuals.We discuss an application based on data from the Italian
survey on small and medium enterprises. By means of a simulation exercise, we explore the
frequentist properties of the estimators proposed. They are good, and differently from methods
based on global shrinkage remain so also for areas characterized by large model residuals.
Lingua originale | English |
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pagine (da-a) | 861-879 |
Numero di pagine | 19 |
Rivista | JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS |
Volume | 2018 |
DOI | |
Stato di pubblicazione | Pubblicato - 2018 |
Keywords
- Local shrinkage priors
- Log-normal distribution
- Regional studies
- Robust estimation
- Variance gamma distribution