Bruce E. Hansen and Jeffrey S. Racine
Bootstrap Model Averaging Unit Root Inference
Advances in Econometrics: Essays in Honor of Subal Kumbhakar
edited by C. Parmeter, M. Tsionas and H. J. Wang.
Forthcoming
Abstract:
Classical unit root tests are known to suffer from potentially crippling size
distortions, and a range of procedures have been proposed to attenuate this
problem, including the use of bootstrap procedures. It is also known that the
estimating equation's functional form can affect the outcome of the test, and
various model selection procedures have been proposed to overcome this
limitation. In this paper, we adopt a model averaging procedure to deal with
model uncertainty at the testing stage. In addition, we leverage an automatic
model-free dependent bootstrap procedure where the null is imposed by simple
differencing (the block length is automatically determined using recent
developments for bootstrapping dependent processes). Monte Carlo simulations
indicate that this approach exhibits the lowest size distortions among its
peers in settings that confound existing approaches, while it has superior
power relative to those peers whose size distortions do not preclude their
general use. The proposed approach is fully automatic, and there are no
nuisance parameters that have to be set by the user, which ought to appeal to practitioners.
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Some of the above material is based upon work supported by the National Science Foundation under Grants No. SES-9022176, SES-9120576, SBR-9412339, and SBR-9807111.
Any opinions, findings, and conclusions, or recommendations expressed in this material are those of the author(s), and do not necessarily reflect the views of the NSF.