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

Danny Pfeffermann, Anna Sikov, Richard Tiller

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

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.

Original languageEnglish
Pages (from-to)631-666
Number of pages36
JournalTest
Volume23
Issue number4
DOIs
StatePublished - Dec 2014
Externally publishedYes

Keywords

  • Autocorrelated sampling errors
  • Generalized least squares
  • Internal benchmarking
  • Optimality
  • Recursive filtering
  • State-space models
  • Trend and seasonal effects

Fingerprint

Dive into the research topics of 'Single- and two-stage cross-sectional and time series benchmarking procedures for small area estimation'. Together they form a unique fingerprint.

Cite this