We examine a kernel regression estimator for time series that takes account of the error correlation structure as proposed by Xiao et al. (2003, Journal of the American Statistical Association 98, 980–992). We show that this method continues to improve estimation in the case where the regressor is a unit root or a near unit root process.
Authors
Oliver Linton
Qiying Wang
Journal article details
- DOI
- 10.1017/S026646661400070X
- Publisher
- Cambridge Journals
- JEL
- C14, C22
- Issue
- Volume 32, Issue 1, February 2016
Suggested citation
Linton, O and Wang, Q. (2016). 'Nonparametric transformation regression with non-stationary data' 32(1/2016)
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