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  Autoregressive wild bootstrap inference for nonparametric trends

Friedrich, M., Smeekes, S., Urbain, J.-P. (2020): Autoregressive wild bootstrap inference for nonparametric trends. - Journal of Econometrics, 214, 1, 81-109.
https://doi.org/10.1016/j.jeconom.2019.05.006

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 Creators:
Friedrich, Marina1, Author              
Smeekes, S.2, Author
Urbain, J.-P.2, Author
Affiliations:
1Potsdam Institute for Climate Impact Research, ou_persistent13              
2External Organizations, ou_persistent22              

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 Abstract: In this paper we propose an autoregressive wild bootstrap method to construct confidence bands around a smooth deterministic trend. The bootstrap method is easy to implement and does not require any adjustments in the presence of missing data, which makes it particularly suitable for climatological applications. We establish the asymptotic validity of the bootstrap method for both pointwise and simultaneous confidence bands under general conditions, allowing for general patterns of missing data, serial dependence and heteroskedasticity. The finite sample properties of the method are studied in a simulation study. We use the method to study the evolution of trends in daily measurements of atmospheric ethane obtained from a weather station in the Swiss Alps, where the method can easily deal with the many missing observations due to adverse weather conditions.

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 Dates: 2020
 Publication Status: Finally published
 Pages: -
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 Rev. Type: Peer
 Identifiers: DOI: 10.1016/j.jeconom.2019.05.006
PIKDOMAIN: RD3 - Transformation Pathways
Organisational keyword: RD3 - Transformation Pathways
eDoc: 8370
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Title: Journal of Econometrics
Source Genre: Journal, SCI, Scopus
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Pages: - Volume / Issue: 214 (1) Sequence Number: - Start / End Page: 81 - 109 Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/journal-of-econometrics
Publisher: Elsevier