Single- and two-stage cross-sectional and time series benchmarking procedures for small area estimation
Single- and two-stage cross-sectional and time series benchmarking procedures for small area estimation
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.
Autocorrelated sampling errors , Generalized least squares , Internal benchmarking, Optimality, Recursive filtering, State-space models, Trend and seasonal effects
631-666
Pfeffermann, Danny
c7fe07a0-9715-42ce-b90b-1d4f2c2c6ffc
Sikov, Anna
81a74f0d-d006-49df-80f5-ed626b989828
Tiller, Richard
3750ee39-e44b-4f1f-a2d4-ec193babca4d
1 December 2014
Pfeffermann, Danny
c7fe07a0-9715-42ce-b90b-1d4f2c2c6ffc
Sikov, Anna
81a74f0d-d006-49df-80f5-ed626b989828
Tiller, Richard
3750ee39-e44b-4f1f-a2d4-ec193babca4d
Pfeffermann, Danny, Sikov, Anna and Tiller, Richard
(2014)
Single- and two-stage cross-sectional and time series benchmarking procedures for small area estimation.
Test, 23 (4), .
(doi:10.1007/s11749-014-0398-y).
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.
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e-pub ahead of print date: 21 October 2014
Published date: 1 December 2014
Keywords:
Autocorrelated sampling errors , Generalized least squares , Internal benchmarking, Optimality, Recursive filtering, State-space models, Trend and seasonal effects
Organisations:
Social Statistics & Demography
Identifiers
Local EPrints ID: 410996
URI: http://eprints.soton.ac.uk/id/eprint/410996
ISSN: 1133-0686
PURE UUID: 0fee5eae-08c6-48dd-8c2f-3faf034acbe5
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Date deposited: 13 Jun 2017 16:31
Last modified: 15 Mar 2024 14:28
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Author:
Anna Sikov
Author:
Richard Tiller
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