Selecting the Order of an ARCH Model
Since the parameters of an autoregressive conditional heteroskedasticity (ARCH) process must be non-negative, inference on ARCH parameters can be improved by using inequality constrained estimation. In this paper, we extend this principle to the problem of ARCH lag order selection. We show that in the case of AIC, the appropriate adjustment to the penalty function has a simple form.
|Date of creation:||1999|
|Date of revision:|
|Publication status:||Published in Economics Letters, 2004, vol. 83, issue 2, pp. 269-275|
|Contact details of provider:|| Postal: Adelaide SA 5005|
Phone: (618) 8303 5540
Web page: http://www.economics.adelaide.edu.au/
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- Lee, John H H & King, Maxwell L, 1993. "A Locally Most Mean Powerful Based Score Test for ARCH and GARCH Regression Disturbances," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(1), pages 17-27, January.
- Granger, Clive W. J. & King, Maxwell L. & White, Halbert, 1995. "Comments on testing economic theories and the use of model selection criteria," Journal of Econometrics, Elsevier, vol. 67(1), pages 173-187, May.
- Tim Bollerslev, 1986.
"Generalized autoregressive conditional heteroskedasticity,"
EERI Research Paper Series
EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
- Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
- Demos, Antonis & Sentana, Enrique, 1998. "Testing for GARCH effects: a one-sided approach," Journal of Econometrics, Elsevier, vol. 86(1), pages 97-127, June.
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