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dc.contributor.authorMa, Junen_US
dc.contributor.authorNelson, Charles R.en_US
dc.contributor.authorStartz, Richarden_US
dc.date.accessioned2009-12-15T21:08:48Z
dc.date.available2009-12-15T21:08:48Z
dc.date.issued2007en_US
dc.identifier.citationJun Ma, Charles R. Nelson, and Richard Startz (2007) "Spurious Inference in the GARCH (1,1) Model When It Is Weakly Identified", Studies in Nonlinear Dynamics and Econometrics: Vol. 11: No. 1, Article 1.en_US
dc.identifier.urihttp://www.bepress.com/snde/vol11/iss1/art1en_US
dc.identifier.urihttp://hdl.handle.net/1773/15537
dc.description.abstractThis paper shows that the Zero-Information-Limit-Condition (ZILC) formulated by Nelson and Startz (2006) holds in the GARCH (1,1) model. As a result, the GARCH estimate tends to have too small a standard error relative to the true one when the ARCH parameter is small, even when sample size becomes very large. In combination with an upward bias in the GARCH estimate, the small standard error will often lead to the spurious inference that volatility is highly persistent when it is not. We develop an empirical strategy to deal with this issue and show how it applies to real datasets.en_US
dc.description.sponsorshipBuechel Memorial Scholarship, Van Voorhis Professorshipen_US
dc.language.isoen_USen_US
dc.titleSpurious Inference in the GARCH (1,1) Model When It Is Weakly Identifieden_US
dc.typeArticleen_US


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