Bootstrap-assisted unit root testing with piecewise locally stationary errors

Yeonwoo Rho, Xiaofeng Shao

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Pages (from-to)143-166
Number of pages24
JournalEconometric Theory
Volume35
Issue number1
DOIs
StatePublished - Feb 1 2019

ASJC Scopus subject areas

  • Social Sciences (miscellaneous)
  • Economics and Econometrics

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