Testing serial dependence or cross dependence for time series with underreporting
Keyao Wei,
Lengyang Wang and
Yingcun Xia
Biometrika, 2024, vol. 111, issue 4, 1293-1312
Abstract:
In practice, it is common for collected data to be underreported, an issue that is particularly prevalent in fields such as the social sciences, ecology and epidemiology. Drawing inferences from such data using conventional statistical methods can lead to incorrect conclusions. In this paper, we study tests for serial or cross dependence in time series data that are subject to underreporting. We introduce new test statistics, develop corresponding group-of-blocks bootstrap techniques and establish their consistency. The methods are shown via simulation studies to be efficient and are used to identify key factors responsible for the spread of dengue fever and the occurrence of cardiovascular disease.
Keywords: Block bootstrap; Lag difference; Local stationarity; Periodic time series; Underreporting (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:
Downloads: (external link)
http://hdl.handle.net/10.1093/biomet/asae027 (application/pdf)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:oup:biomet:v:111:y:2024:i:4:p:1293-1312.
Ordering information: This journal article can be ordered from
https://academic.oup.com/journals
Access Statistics for this article
Biometrika is currently edited by Paul Fearnhead
More articles in Biometrika from Biometrika Trust Oxford University Press, Great Clarendon Street, Oxford OX2 6DP, UK.
Bibliographic data for series maintained by Oxford University Press ().