Spurious Inference in the GARCH (1,1) Model When It Is Weakly Identified

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Spurious Inference in the GARCH (1,1) Model When It Is Weakly Identified

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dc.contributor.author Ma, Jun en_US
dc.contributor.author Nelson, Charles R. en_US
dc.contributor.author Startz, Richard en_US
dc.date.accessioned 2009-12-15T21:08:48Z
dc.date.available 2009-12-15T21:08:48Z
dc.date.issued 2007 en_US
dc.identifier.citation Jun 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.uri http://www.bepress.com/snde/vol11/iss1/art1 en_US
dc.identifier.uri http://hdl.handle.net/1773/15537
dc.description.abstract This 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.sponsorship Buechel Memorial Scholarship, Van Voorhis Professorship en_US
dc.language.iso en_US en_US
dc.title Spurious Inference in the GARCH (1,1) Model When It Is Weakly Identified en_US
dc.type Article en_US


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