Bootstrap-assisted unit root testing with piecewise locally stationary errors
Document Type
Article
Publication Date
2-1-2019
Abstract
© 2018 Cambridge University Press. In unit root testing, a piecewise locally stationary process is adopted to accommodate nonstationary errors that can have both smooth and abrupt changes in second-or higher-order properties. Under this 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 bootstrap consistency. The proposed method is compared through finite sample simulations with the recolored wild bootstrap procedure, which was developed for errors that follow a heteroscedastic linear process. Furthermore, a combination of autoregressive sieve recoloring with the dependent wild bootstrap is shown to perform well. The validity of the dependent wild bootstrap in a nonstationary setting is demonstrated for the first time, showing the possibility of extensions to other inference problems associated with locally stationary processes.
Publication Title
Econometric Theory
Recommended Citation
Rho, Y.,
&
Shao, X.
(2019).
Bootstrap-assisted unit root testing with piecewise locally stationary errors.
Econometric Theory,
35(1), 143-166.
http://doi.org/10.1017/S0266466618000038
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/7754
