Single- and two-stage cross-sectional and time series benchmarking procedures for small area estimation

Danny Pfeffermann (), Anna Sikov and Richard Tiller

TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, 2014, vol. 23, issue 4, 666 pages

Abstract: This article is divided into two parts. In the first part, we review and study the properties of single-stage cross-sectional and time series benchmarking procedures that have been proposed in the literature in the context of small area estimation. We compare cross-sectional and time series benchmarking empirically, using data generated from a time series model which complies with the familiar Fay–Herriot model at any given time point. In the second part, we review cross-sectional methods proposed for benchmarking hierarchical small areas and develop a new two-stage benchmarking procedure for hierarchical time series models. The latter procedure is applied to monthly unemployment estimates in Census Divisions and States of the USA. Copyright Sociedad de Estadística e Investigación Operativa 2014

Keywords: Autocorrelated sampling errors; Generalized least squares; Internal benchmarking; Optimality; Recursive filtering; State-space models; Trend and seasonal effects; 37M10 (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (10)

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DOI: 10.1007/s11749-014-0398-y

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