Template-type: ReDif-Paper 1.0
Author-Name: Friedrich, Marina
Author-workplace-name: QE Econometrics, RS: GSBE ETBC
Author-Name: Smeekes, Stephan
Author-workplace-name: QE Econometrics, RS: GSBE EFME
Author-Name: Urbain, Jean-Pierre
Author-workplace-name: RS: GSBE EFME
Title: Autoregressive Wild Bootstrap Inference for Nonparametric Trends
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.
Classification-JEL: c14,c22
Series: GSBE Research Memoranda
Creation-Date: 20170501
Number: 010
File-URL: https://cris.maastrichtuniversity.nl/ws/files/12332690/RM17010.pdf
File-Format: application/pdf
File-Size: 787070
Handle: Repec:unm:umagsb:2017010
DOI: 10.26481/umagsb.2017010