Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/207036
Authors: 
Burgard, Jan Pablo
Krause, Joscha
Kreber, Dennis
Year of Publication: 
2019
Series/Report no.: 
Research Papers in Economics No. 4/19
Publisher: 
Universität Trier, Fachbereich IV - Volkswirtschaftslehre, Trier
Abstract: 
An approach to model-based small area estimation under covariate measurement errors is presented. Using a min-max approach, we proof that regularized regression coefficient estimation is equivalent to robust optimization under additive noise. Applying this equivalence, the Fay-Herriot model is extended by l1-norm, squared l2-norm and elastic net regularizations as robustification against design matrix perturbations. This allows for reliable area-statistic estimates without distributive information about the measurement errors. A best predictor and a Jackknife estimator of the mean squared error are presented. The methodology is evaluated in a simulation study under multiple measurement error scenarios to support the theoretical findings. A comparison to other robust small area approaches is conducted. An empirical application to poverty mapping in the US is provided. Estimated economic figures from the US Census Bureau and crime records from the Uniform Crime Reporting Program are used to model the number of citizens below the federal poverty threshold.
Subjects: 
min-max
pathwise coordinate descent
regularized least squares
robust optimization
Document Type: 
Working Paper

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