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Testing for a unit root in a nonlinear quantile autoregression framework

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  • Haiqi Li
  • Sung Y. Park

Abstract

The nonlinear unit root test of Kapetanios, Shin, and Snell (2003) (KSS) has attracted much recent attention. However, the KSS test relies on the ordinary least squares (OLS) estimator, which is not robust to a heavy-tailed distribution and, in practice, the test suffers from a large power loss. This study develops three kinds of quantile nonlinear unit root tests: the quantile t-ratio test; the quantile Kolmogorov–Smirnov test; and the quantile Cramer–von Mises test. A Monte Carlo simulation shows that these tests have significantly better power when an innovation follows a non-normal distribution. In addition, the quantile t-ratio test can reveal the heterogeneity of the asymmetric dynamics in a time series. In our empirical studies, we investigate the unit root properties of U.S. macroeconomic time series and the real effective exchange rates for 61 countries. The results show that our proposed tests reject the unit roots more often, indicating that the series are likely to be asymmetric nonlinear reverting processes.

Suggested Citation

  • Haiqi Li & Sung Y. Park, 2018. "Testing for a unit root in a nonlinear quantile autoregression framework," Econometric Reviews, Taylor & Francis Journals, vol. 37(8), pages 867-892, September.
    • Handle: RePEc:taf:emetrv:v:37:y:2018:i:8:p:867-892
    DOI: 10.1080/00927872.2016.1178871
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    Citations

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    Cited by:

    1. Mohsen Bahmani‐Oskooee & Tsangyao Chang & Farhang Niroomand & Omid Ranjbar, 2020. "Fourier nonlinear quantile unit root test and PPP in Africa," Bulletin of Economic Research, Wiley Blackwell, vol. 72(4), pages 451-481, October.
    2. Yuan, Di & Li, Sufang & Li, Rong & Zhang, Feipeng, 2022. "Economic policy uncertainty, oil and stock markets in BRIC: Evidence from quantiles analysis," Energy Economics, Elsevier, vol. 110(C).
    3. Yi‐Ting Peng & Tsangyao Chang & Omid Ranjbar, 2022. "Analyzing the degree of persistence of economic policy uncertainty using linear and non‐linear fourier quantile unit root tests," Manchester School, University of Manchester, vol. 90(4), pages 453-471, July.
    4. Yang, Yang & Zhao, Zhao, 2020. "Quantile nonlinear unit root test with covariates and an application to the PPP hypothesis," Economic Modelling, Elsevier, vol. 93(C), pages 728-736.
    5. Nazlioglu, Saban & Kucukkaplan, Ilhan & Kilic, Emre & Altuntas, Mehmet, 2022. "Financial market integration of emerging markets: Heavy tails, structural shifts, nonlinearity, and asymmetric persistence," Research in International Business and Finance, Elsevier, vol. 62(C).
    6. Badri Narayan Rath & Vaseem Akram, 2021. "Popularity of Unit Root Tests - A Review," Asian Economics Letters, Asia-Pacific Applied Economics Association, vol. 2(4), pages 1-5.
    7. Li, Haiqi & Zheng, Chaowen, 2018. "Unit root quantile autoregression testing with smooth structural changes," Finance Research Letters, Elsevier, vol. 25(C), pages 83-89.
    8. Badri Narayan Rath & Vaseem Akram, 2022. "Popularity of Unit Root Tests - A Review," Asian Economics Letters, Asia-Pacific Applied Economics Association, vol. 2(4), pages 1-5.
    9. Yang, Jisheng & Wei, Jinbao & Cai, Biqing, 2022. "Quantile unit root inference for panel data with common shocks," Economics Letters, Elsevier, vol. 219(C).

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