Spurious Inference in the GARCH (1,1) Model When It Is Weakly IdentifiedStudies in Nonlinear Dynamics & Econometrics (2007)
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.
- weak identification,
- conditional heteroskedasticity
Publication DateMarch, 2007
Citation InformationJun Ma, Charles R Nelson and Richard Startz. "Spurious Inference in the GARCH (1,1) Model When It Is Weakly Identified" Studies in Nonlinear Dynamics & Econometrics Vol. 11 Iss. 1 (2007)
Available at: http://works.bepress.com/junma/1/