Autoregressive wild bootstrap inference for nonparametric trends

Marina Friedrich, Stephan Smeekes, Jean Pierre Urbain

Research output: Contribution to JournalArticleAcademicpeer-review

36 Downloads (Pure)


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.

Original languageEnglish
Pages (from-to)81-109
Number of pages29
JournalJournal of Econometrics
Issue number1
Early online date8 Aug 2019
Publication statusPublished - Jan 2020


  • Nonparametric Estimation
  • Bootstrap
  • Confidence Intervals
  • Trend analysis
  • Missing data


Dive into the research topics of 'Autoregressive wild bootstrap inference for nonparametric trends'. Together they form a unique fingerprint.

Cite this