Autoregressive Wild Bootstrap Inference for Nonparametric Trends

Marina Friedrich (), Stephan Smeekes and Jean-Pierre Urbain

Papers from arXiv.org

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.

Date: 2018-07, Revised 2019-11
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Citations: View citations in EconPapers (2)

Published in Journal of Econometrics 214 (2020) 81-109

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http://arxiv.org/pdf/1807.02357 Latest version (application/pdf)

Related works:
Journal Article: Autoregressive wild bootstrap inference for nonparametric trends (2020)
Working Paper: Autoregressive Wild Bootstrap Inference for Nonparametric Trends (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1807.02357

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