Optimal Versus Robust Inference in Nearly Integrated Non Gaussian Models

32 Pages Posted: 25 Apr 2003

Date Written: April 2003

Abstract

Elliott, Rothenberg and Stock (1996) derived a class of point-optimal unit root tests in a time series model with Gaussian errors. Other authors have proposed "robust" tests which are not optimal for any model but perform well when the error distribution has thick tails. I derive a class of point-optimal tests for models with non Gaussian errors. When the true error distribution is known and has thick tails, the point-optimal tests are generally more powerful than Elliott et al.'s (1996) tests as well as the robust tests. However, when the true error distribution is unknown and asymmetric, the point-optimal tests can behave very badly. Thus there is a tradeoff between robustness to unknown error distributions and optimality with respect to the trend coefficients.

Keywords: Near Unit Root, Robust Tests, Optimal Tests

Suggested Citation

Thompson, Samuel Brodsky, Optimal Versus Robust Inference in Nearly Integrated Non Gaussian Models (April 2003). Available at SSRN: https://ssrn.com/abstract=399000 or http://dx.doi.org/10.2139/ssrn.399000

Samuel Brodsky Thompson (Contact Author)

Arrowstreet Capital, L.P. ( email )

44 Brattle St., 5th Floor
Cambridge, MA 02138
United States
617 349 2254 (Phone)

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