Autoregressive Wild Bootstrap Inference for Nonparametric Trends

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

No 10, Research Memorandum from Maastricht University, Graduate School of Business and Economics (GSBE)

Abstract: In this paper a modified wild bootstrap method is presented to construct pointwise confidence intervals around a nonparametric deterministic trend model. We derive the asymptotic distribution of a nonparametric kernel estimator of the trend function under general conditions, which allow for serial correlation and heteroskedasticity. Asymptotic validity of the bootstrap method is established and it is shown to work well in finite samples in an extensive simulation study. The bootstrap method has the potential of providing simultaneous confidence bands for the same models along the lines of Bühlmann (1998) and can be applied without further adjustments to missing data. We illustrate this by applying the proposed method to a time series of atmospheric ethane which can be used as an indicator of atmospheric pollution and transport.

JEL-codes: C14 C22 (search for similar items in EconPapers)
Date: 2017-05-01
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
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Citations: View citations in EconPapers (2)

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Related works:
Journal Article: Autoregressive wild bootstrap inference for nonparametric trends (2020)
Working Paper: Autoregressive Wild Bootstrap Inference for Nonparametric Trends (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:unm:umagsb:2017010

DOI: 10.26481/umagsb.2017010

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