Outlier robust small area estimation under spatial correlation

Timo Schmid, Nikos Tzavidis, Ralf Münnich and Ray Chambers

No 2015/8, Discussion Papers from Free University Berlin, School of Business & Economics

Abstract: Modern systems of official statistics require the estimation and publication of business statistics for disaggregated domains, for example, industry domains and geographical regions. Outlier robust methods have proven to be useful for small area estimation. Recently proposed outlier robust modelbased small area methods assume, however, uncorrelated random effects. Spatial dependencies, resulting from similar industry domains or geographic regions, often occur. In this paper we propose outlier robust small area methodology that allows for the presence of spatial correlation in the data. In particular, we present a robust predictive methodology that incorporates the potential spatial impact from other areas (domains) on the small area (domain) of interest. We further propose two parametric bootstrap methods for estimating the mean-squared error. Simulations indicate that the proposed methodology may lead to efficiency gains. The paper concludes with an illustrative application by using business data for estimating average labour costs in Italian provinces.

Keywords: bias correction; projective and predictive estimators; spatial correlation; business surveys (search for similar items in EconPapers)
Date: 2015
New Economics Papers: this item is included in nep-ecm, nep-geo and nep-ure
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:fubsbe:20158

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