This paper proposes new unit root tests that are more powerful when the error term follows a non-normal distribution. The improved power is gained by utilizing the additional moment conditions embodied in non-normal errors. Speci…cally, we follow the work of Im and Schmidt (2008), using the framework of generalized methods of moments (GMM), and adopt a simple two-step procedure based on the "residual augmented least squares" (RALS) methodology. Our RALS-based unit root tests make use of non-linear moment conditions through a computationally simple procedure. Our Monte Carlo simulation results show that the RALS-based unit root tests have good size and power properties, and they show signi…cant e¢ ciency gains when utilizing the additional information contained in non-normal errors-information that is ignored in traditional unit root tests. JEL Classi…cation: C22, C12, C13.
CITATION STYLE
Im, K. S., Lee, J., & Tieslau, M. A. (2014). More Powerful Unit Root Tests with Non-normal Errors. In Festschrift in Honor of Peter Schmidt (pp. 315–342). Springer New York. https://doi.org/10.1007/978-1-4899-8008-3_10
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