Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/207046 
Year of Publication: 
2019
Series/Report no.: 
Research Papers in Economics No. 14/19
Publisher: 
Universität Trier, Fachbereich IV - Volkswirtschaftslehre, Trier
Abstract: 
Model-based small area predictors are derived under the assumption that data files are complete. In application to real data, files may contain missing values. We introduce a variant of the bivariate Fay-Herriot model that takes into account for missing values in one component of the target variable and give fitting algorithms to estimate the model parameters. Based on the new model, we introduce empirical best predictors of domain means and derive an approximation to the mean squared error.
Subjects: 
Multivariate models
Fay-Herriot model
small area estimation
missing values
Document Type: 
Working Paper

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