Spurious Inference in the GARCH (1,1) Model When It Is Weakly Identified
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.
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Bibliographic InfoArticle provided by De Gruyter in its journal Studies in Nonlinear Dynamics & Econometrics.
Volume (Year): 11 (2007)
Issue (Month): 1 (March)
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Web page: http://www.degruyter.com
Other versions of this item:
- Jun Ma & Charles Nelson & Richard Startz, 2007. "Spurious Inference in the GARCH(1,1) Model When It Is Weakly Identified," Working Papers, University of Washington, Department of Economics UWEC-2006-14-P, University of Washington, Department of Economics, revised Mar 2007.
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
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- Donald W.K. Andrews & Patrik Guggenberger, 2011.
"A Conditional-Heteroskedasticity-Robust Confidence Interval for the Autoregressive Parameter,"
Cowles Foundation Discussion Papers, Cowles Foundation for Research in Economics, Yale University
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- Donald W.K. Andrews & Patrik Guggenberger, 2011. "A Conditional-Heteroskedasticity-Robust Confidence Interval for the Autoregressive Parameter," Cowles Foundation Discussion Papers, Cowles Foundation for Research in Economics, Yale University 1812, Cowles Foundation for Research in Economics, Yale University.
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- Liu, Yan & Luger, Richard, 2009. "Efficient estimation of copula-GARCH models," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 53(6), pages 2284-2297, April.
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