Unit root testing on buffered autoregressive model

Di WANG, Wai Keung LI

Research output: Contribution to journalArticlespeer-review


A buffered autoregression extends the classical threshold autoregression by allowing a buffer region for regime changes. In this study, we examine asymptotic statistical inferences for the two-regime buffered autoregressive (BAR) model, with autoregressive unit roots. We propose a Sup-LR test for the nonlinear buffer effect in the possible presence of unit roots, and a class of unit root tests to identify the number of nonstationary regimes in the BAR model. The wild bootstrap method is suggested to approximate the critical values of the two tests. Simulation results show that the proposed unit root test outperforms the conventional augmented Dickey-Fuller test, and that the two wild bootstrap tests are robust to unknown heteroscedasticity. Two macroeconomic data examples, based on U.S. unemployment rates and real exchange rates, respectively, are provided to illustrate the methods. Copyright © 2020 Institute of Statistical Science, Academia Sinica.
Original languageEnglish
Pages (from-to)977-1003
JournalStatistica Sinica
Issue number2
Publication statusPublished - Apr 2020


Wang, D., & Li, W. K. (2020). Unit root testing on buffered autoregressive model. Statistica Sinica, 30(2), 977-1003. doi: 10.5705/ss.202017.0507


  • Asymptotic theory
  • Buffer effect
  • Nonlinear time series
  • Nonstationary
  • Threshold autoregression
  • Wild bootstrap


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