A New Approach to Robust Inference in Cointegration

Sainan Jin, Peter C. B. Phillips () and Yixiao Sun ()
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Sainan Jin: Guanghua School of Management, Peking University

No 1538, Cowles Foundation Discussion Papers from Cowles Foundation, Yale University

Abstract: A new approach to robust testing in cointegrated systems is proposed using nonparametric HAC estimators without truncation. While such HAC estimates are inconsistent, they still produce asymptotically pivotal tests and, as in conventional regression settings, can improve testing and inference. The present contribution makes use of steep origin kernels which are obtained by exponentiating traditional quadratic kernels. Simulations indicate that tests based on these methods have improved size properties relative to conventional tests and better power properties than other tests that use Bartlett or other traditional kernels with no truncation.

Keywords: Cointegration; HAC estimation; long-run covariance matrix; robust inference; steep origin kernel; fully modified estimation (search for similar items in EconPapers)
JEL-codes: C12 C14 C22 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm and nep-ets
Date: 2005-10
Note: CFP 1203.
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Published in Economics Letters (May 2006), 91(2): 300-306

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Journal Article: A new approach to robust inference in cointegration (2006)
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