Bootstrapping Neural tests for conditional heteroskedasticity
We deal with bootstrapping tests for detecting conditional heteroskedasticity in the context of standard and nonstandard ARCH models. We develope parametric and nonparametric bootstrap tests based both on the LM statistic and a neural statistic. The neural tests are designed to approximate an arbitrary nonlinear form of the conditional variance by a neural function. While published tests are valid asymptotically, they are not exact in finite samples and suffer from a substantial size distortion: the finite-sample error remains non-negligible, even for several hundred observations. Here, we treat this problem using bootstrap methods, making possible a better finite-sample estimate of the distribution of the test statistic. A graphical presentation employing a size-correction principle is used to show the true power of the tests rather than the spurious nominal power typically given
|Date of creation:||04 Jul 2006|
|Date of revision:|
|Contact details of provider:|| Web page: http://comp-econ.org/|
More information through EDIRC
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Peguin-Feissolle, Anne, 1999.
"A comparison of the power of some tests for conditional heteroscedasticity,"
Elsevier, vol. 63(1), pages 5-17, April.
- Anne Peguin-Feissolle, 1999. "A comparison of the power of some tests for conditional heteroscedasticity," Post-Print halshs-00390157, HAL.
- Peguin-Feissolle, A., 1999. "A Comparison of the Power of Some Tests for Conditional Heteroscedasticity," G.R.E.Q.A.M. 99a22, Universite Aix-Marseille III.
- Weber, N. C., 1984. "On resampling techniques for regression models," Statistics & Probability Letters, Elsevier, vol. 2(5), pages 275-278, October.
- Fama, Eugene F. & French, Kenneth R., 1993. "Common risk factors in the returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 33(1), pages 3-56, February.
- Lee, Tae-Hwy & White, Halbert & Granger, Clive W. J., 1993.
"Testing for neglected nonlinearity in time series models : A comparison of neural network methods and alternative tests,"
Journal of Econometrics,
Elsevier, vol. 56(3), pages 269-290, April.
- Tom Doan, . "REGWHITENNTEST: RATS procedure to perform White neural network test on regression," Statistical Software Components RTS00183, Boston College Department of Economics.
- Tom Doan, . "REGRESET: RATS procedure to perform Ramsey RESET test on regression," Statistical Software Components RTS00181, Boston College Department of Economics.
- Joel L. Horowitz, 1996. "Bootstrap Methods in Econometrics: Theory and Numerical Performance," Econometrics 9602009, EconWPA, revised 05 Mar 1996.
- Horowitz, Joel L., 1994. "Bootstrap-based critical values for the information matrix test," Journal of Econometrics, Elsevier, vol. 61(2), pages 395-411, April.
When requesting a correction, please mention this item's handle: RePEc:sce:scecfa:301. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Christopher F. Baum)
If references are entirely missing, you can add them using this form.