Time series modelling of repeated survey data for estimation of finite population parameters
Time series modelling of repeated survey data for estimation of finite population parameters
In the first part of the article, I review and discuss the pioneering contributions of the late Alastair Scott and T.M.F Smith to time series analysis of repeated survey data. In the second part, I review and discuss some of the extensive theoretical and applied developments in this area, emerging from their work over the ensuing 40 years or so. I conclude with a brief summary of Scott and Smith contributions and extensions, with some remarks on possible advances and challenges to time series analysis of repeated surveys.
autocorrelated sampling errors, basic structural model, benchmarking, big data, covid-19, design-based estimation, state-space models
1757-1777
Pfeffermann, Danny
c7fe07a0-9715-42ce-b90b-1d4f2c2c6ffc
October 2022
Pfeffermann, Danny
c7fe07a0-9715-42ce-b90b-1d4f2c2c6ffc
Pfeffermann, Danny
(2022)
Time series modelling of repeated survey data for estimation of finite population parameters.
Journal of the Royal Statistical Society: Series A (Statistics in Society), 185 (4), .
(doi:10.1111/rssa.12950).
Abstract
In the first part of the article, I review and discuss the pioneering contributions of the late Alastair Scott and T.M.F Smith to time series analysis of repeated survey data. In the second part, I review and discuss some of the extensive theoretical and applied developments in this area, emerging from their work over the ensuing 40 years or so. I conclude with a brief summary of Scott and Smith contributions and extensions, with some remarks on possible advances and challenges to time series analysis of repeated surveys.
Text
Repeated Surveys third revision
- Accepted Manuscript
Text
Time series modelling
- Version of Record
Restricted to Repository staff only
Request a copy
More information
Accepted/In Press date: 5 August 2022
e-pub ahead of print date: 11 October 2022
Published date: October 2022
Additional Information:
Funding Information:
I am grateful to Jan Brakel van den from Statistics Netherlands and William Bell from the Census Bureau in the United States for sharing with me their important contributions related to the topic of this article.
Publisher Copyright:
© 2022 Royal Statistical Society.
Keywords:
autocorrelated sampling errors, basic structural model, benchmarking, big data, covid-19, design-based estimation, state-space models
Identifiers
Local EPrints ID: 472650
URI: http://eprints.soton.ac.uk/id/eprint/472650
ISSN: 0964-1998
PURE UUID: 7a42040f-3cd8-4ec9-959d-d2dff09aa5f0
Catalogue record
Date deposited: 13 Dec 2022 17:37
Last modified: 17 Mar 2024 07:32
Export record
Altmetrics
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics