Testing time series data compatibility for benchmarking

Compatibility testing determines whether two series, say a sub-annual and an annual series, both of which are subject to sampling errors, can be considered suitable for benchmarking. We derive statistical tests and discuss the issues with their implementation. The results are illustrated using the a...

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Bibliographic Details
Published inInternational journal of forecasting Vol. 29; no. 4; pp. 754 - 766
Main Authors Quennevillle, Benoît, Gagné, Christian
Format Journal Article
LanguageEnglish
Published Amsterdam Elsevier B.V 01.10.2013
Elsevier Sequoia S.A
Subjects
Autoregressive process
Benchmarks
Chi-square distribution
Likelihood ratio test
Sampling techniques
Score test
Studies
Test methods
Time series
Wald test
Autoregressive process
Score test
Wald test
Chi-square distribution
Likelihood ratio test
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Summary:Compatibility testing determines whether two series, say a sub-annual and an annual series, both of which are subject to sampling errors, can be considered suitable for benchmarking. We derive statistical tests and discuss the issues with their implementation. The results are illustrated using the artificial series from Denton (1971) and two empirical examples. A practical way of implementing the tests is also presented.
Bibliography:SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ISSN:0169-2070
1872-8200
DOI:10.1016/j.ijforecast.2011.10.001