Wild bootstrap tests for unit root in ESTAR models

This paper introduces wild bootstrap tests for unit root in exponential smooth transition autoregressive (ESTAR) models. Asymptotic unit root tests in ESTAR models have severe size distortions in the presence of heteroskedastic variances such as generalized autoregressive conditional heteroskedasticity and stochastic volatility, and hence, to improve these distortions, we use a wild bootstrap. Monte Carlo simulations show that in asymptotic tests, severe over-rejection of the null hypothesis occurs under heteroskedastic variances, whereas the proposed wild bootstrap tests have reasonable size and power properties.