A0487
Title: Bootstrap-assisted unit root testing with piecewise locally stationary errors
Authors: Yeonwoo Rho - Michigan Technological University (United States) [presenting]
Xiaofeng Shao - University of Illinois at Urbana-Champaign (United States)
Abstract: In the unit root testing, the piecewise locally stationary process is adopted to accommodate nonstationary errors that can have smooth and abrupt changes in second or higher order properties. Under this new framework, the limiting null distributions of the conventional unit root test statistics are derived and shown to contain a number of unknown parameters. To circumvent the difficulty of direct consistent estimation, we propose to use the dependent wild bootstrap to approximate the nonpivotal limiting null distributions and provide a rigorous theoretical justification for the bootstrap consistency. The proposed method is compared with the recolored wild bootstrap procedure, which was developed for the error that follows a heteroscedastic linear process through finite sample simulations. Further, a combination of autoregressive sieve recoloring with the dependent wild bootstrap is shown to perform well. The validity of the dependent wild bootstrap in the nonstationary setting is revealed for the first time, showing the possibility of extensions to other inference problems associated with locally stationary processes.
