A Conditional-Heteroskedasticity-Robust Confidence Interval for the Autoregressive Parameter
This paper introduces a new conﬁdence interval (CI) for the autoregressive parameter (AR) in an AR(1) model that allows for conditional heteroskedasticity of general form and AR parameters that are less than or equal to unity. The CI is a modiﬁcation of Mikusheva’s (2007a) modiﬁcation of Stock’s (1991) CI that employs the least squares estimator and a heteroskedasticity-robust variance estimator. The CI is shown to have correct asymptotic size and to be asymptotically similar (in a uniform sense). It does not require any tuning parameters. No existing procedures have these properties. Monte Carlo simulations show that the CI performs well in ﬁnite samples in terms of coverage probability and average length, for innovations with and without conditional heteroskedasticity.
Andrews, Donald W.K. and Guggenberger, Patrik, "A Conditional-Heteroskedasticity-Robust Confidence Interval for the Autoregressive Parameter" (2011). Cowles Foundation Discussion Papers. 2158.