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Studies in Nonlinear Dynamics & Econometrics

Ed. by Mizrach, Bruce

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Volume 11, Issue 1 (Mar 2007)


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

Jun Ma
  • 1University of Washington,
/ Charles R Nelson
  • 2University of Washington,
/ Richard Startz
  • 3University of Washington,
Published Online: 2007-03-01 | DOI: https://doi.org/10.2202/1558-3708.1434

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.

About the article

Published Online: 2007-03-01

Citation Information: Studies in Nonlinear Dynamics & Econometrics, ISSN (Online) 1558-3708, DOI: https://doi.org/10.2202/1558-3708.1434. Export Citation

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