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 InfoPaper provided by University of Washington, Department of Economics in its series Working Papers with number UWEC-2006-14-P.
Date of creation: Mar 2007
Date of revision: Mar 2007
Publication status: Published in Studies in Nonlinear Dynamics & Econometrics, Volume Vol.11, Article 1
Other versions of this item:
- Ma Jun & Nelson Charles R & Startz Richard, 2007. "Spurious Inference in the GARCH (1,1) Model When It Is Weakly Identified," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 11(1), pages 1-27, March.
- 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
This paper has been announced in the following NEP Reports:
- NEP-ALL-2007-05-12 (All new papers)
- NEP-ECM-2007-05-12 (Econometrics)
- NEP-ETS-2007-05-12 (Econometric Time Series)
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- Donald W.K. Andrews & Patrik Guggenberger, 2011.
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