Non-parametric seasonal unit root tests under periodic non-stationary volatility

• Published in: Computational statistics 2022-11, Vol.37 (5), p.2581-2636
• Main Authors: Gög̃ebakan, Kemal Çag̃lar, Eroglu, Burak Alparslan
• Format: Article
• Language: English
• Subjects: Cancer; Economic Theory/Quantitative Economics/Mathematical Methods; Innovations; Mathematics and Statistics; Monte Carlo simulation; Nonparametric statistics; Original Paper; Probability and Statistics in Computer Science; Probability Theory and Stochastic Processes; Regression analysis; Resampling; Statistics; Variables; Volatility
• ISSN: 0943-4062; 1613-9658
• DOI: 10.1007/s00180-022-01211-w

This paper presents a new non-parametric seasonal unit root testing framework that is robust to periodic non-stationary volatility in innovation variance by making an extension to the fractional seasonal variance ratio unit root tests of Eroğlu et al. (Econ Lett 167:75–80, 2018). The setup allows for both periodic heteroskedasticity structure of Burridge and Taylar (J Econ 104(1):91–117, 2001) and non-stationary volatility structure of Cavaliere and Taylor (Econ Theory 24(1):43-71, 2008). We show that the limiting null distributions of the variance ratio tests depend on nuisance parameters derived from the underlying volatility process. Monte Carlo simulations show that the standard variance ratio tests can be substantially oversized in the presence of such effects. Consequently, we propose wild bootstrap implementations of the variance ratio tests. Wild bootstrap resampling schemes are shown to deliver asymptotically pivotal inference. The simulation evidence depicts that the proposed bootstrap tests perform well in practice and essentially correct the size problems observed in the standard fractional seasonal variance ratio tests, even under extreme patterns of heteroskedasticity.