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Selecting the Order of an ARCH Model

  • Anthony W. Hughes

    (Department of Economics, University of Adelaide)

  • Maxwell L. King

    (Monash University)

  • Kwek Kian Teng

    (Monash University)

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.

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File URL: http://www.economics.adelaide.edu.au/research/papers/doc/wp1999-01.pdf
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Paper provided by University of Adelaide, School of Economics in its series School of Economics Working Papers with number 1999-01.

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Length: 8 pages
Date of creation: 1999
Date of revision:
Publication status: Published in Economics Letters, 2004, vol. 83, issue 2, pp. 269-275
Handle: RePEc:adl:wpaper:1999-01
Contact details of provider: Postal: Adelaide SA 5005
Phone: (618) 8303 5540
Web page: http://www.economics.adelaide.edu.au/

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  1. Tim Bollerslev, 1986. "Generalized autoregressive conditional heteroskedasticity," EERI Research Paper Series EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
  2. 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.
  3. Demos, Antonis & Sentana, Enrique, 1998. "Testing for GARCH effects: a one-sided approach," Journal of Econometrics, Elsevier, vol. 86(1), pages 97-127, June.
  4. 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.
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