Data sharpening has been shown to reduce bias in nonparametric
regression and density estimation. Its performance on nonlinear first
order autoregressive models is studied theoretically and numerically
in this paper. Although the asymptotic properties of data sharpen-
ing are not as favourable in the presence of serial dependence as
in bivariate regression with independent responses, it is still found
to reduce bias under mild conditions on the autoregression function.
Numerical comparisons with the bias reduction method of Cheng et al.
(2018) indicate that data sharpening is competitive in this setting.
Keywords: nonparametric regression, bias reduction, kernel, autoregressive
time series