Spurious Inference in the Garch(1,1) Model When it is Weakly Identified

28 Pages Posted: 7 Jun 2006

See all articles by Jun Ma

Jun Ma

Northeastern University - Department of Economics

Charles R. Nelson

Dept of Economics

Richard Startz

UCSB

Date Written: December 1, 2006

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

JEL Classification: C12, C22

Suggested Citation

Ma, Jun and Nelson, Charles R. and Startz, Richard, Spurious Inference in the Garch(1,1) Model When it is Weakly Identified (December 1, 2006). Studies in Nonlinear Dynamics and Econometrics, Vol. 11, No. 1, 2007, Available at SSRN: https://ssrn.com/abstract=906689

Jun Ma (Contact Author)

Northeastern University - Department of Economics ( email )

301 Lake Hall
360 Huntington Avenue
Boston, MA MA 02446
United States

Charles R. Nelson

Dept of Economics ( email )

Box 353330
Seattle, WA 98195-3330
United States

Richard Startz

UCSB ( email )

Department of Economics
University of California
Santa Barbara, CA 93106-9210
United States
805-893-2895 (Phone)

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