Optimal Versus Robust Inference in Nearly Integrated Non Gaussian Models

32 Pages Posted: 25 Apr 2003

Date Written: April 2003


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). Harvard Institute of Economic Research Discussion Paper No. 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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