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Spurious Inference in the GARCH (1,1) Model When It Is Weakly Identified
Studies in Nonlinear Dynamics & Econometrics (2007)
  • Jun Ma, University of Washington
  • Charles R Nelson, University of Washington
  • Richard Startz, University of Washington
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.
Keywords
  • weak identification,
  • GARCH,
  • conditional heteroskedasticity
Publication Date
March, 2007
Citation Information
Jun 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/