Deutsch
 
Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT

Freigegeben

Zeitschriftenartikel

Autoregressive wild bootstrap inference for nonparametric trends

Urheber*innen
/persons/resource/marina.friedrich

Friedrich,  Marina
Potsdam Institute for Climate Impact Research;

Smeekes,  S.
External Organizations;

Urbain,  J.-P.
External Organizations;

Externe Ressourcen
Es sind keine externen Ressourcen hinterlegt
Volltexte (frei zugänglich)

22936oa.pdf
(Postprint), 717KB

Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar
Zitation

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


Zitierlink: https://publications.pik-potsdam.de/pubman/item/item_22936
Zusammenfassung
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.