More Powerful Unit Root Tests with Non-Normal Errors

31 Pages Posted: 17 Dec 2009

See all articles by Kyung So Im

Kyung So Im

affiliation not provided to SSRN

Junsoo Lee

University of Alabama - Department of Economics, Finance and Legal Studies

Margie Tieslau

University of North Texas - Department of Economics

Date Written: November 8, 2009

Abstract

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. Specifi…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 significant efficiency gains when utilizing the additional information contained in non-normal errors information that is ignored in traditional unit root tests.

Suggested Citation

Im, Kyung So and Lee, Junsoo and Tieslau, Margie, More Powerful Unit Root Tests with Non-Normal Errors (November 8, 2009). Available at SSRN: https://ssrn.com/abstract=1523243 or http://dx.doi.org/10.2139/ssrn.1523243

Kyung So Im (Contact Author)

affiliation not provided to SSRN ( email )

Junsoo Lee

University of Alabama - Department of Economics, Finance and Legal Studies ( email )

P.O. Box 870244
Tuscaloosa, AL Alabama 35487
United States
2053488978 (Phone)

Margie Tieslau

University of North Texas - Department of Economics ( email )

Denton, TX 76203-1457
United States

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