Bootstrapping Neural tests for conditional heteroskedasticity
AbstractWe 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
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Society for Computational Economics in its series Computing in Economics and Finance 2006 with number 301.
Date of creation: 04 Jul 2006
Date of revision:
Bootstrap; Artificial Neural Networks; ARCH models; inference tests;
Find related papers by JEL classification:
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
This paper has been announced in the following NEP Reports:
- NEP-ALL-2006-07-15 (All new papers)
- NEP-CMP-2006-07-15 (Computational Economics)
- NEP-ECM-2006-07-15 (Econometrics)
- NEP-ETS-2006-07-15 (Econometric Time Series)
- NEP-ICT-2006-07-15 (Information & Communication Technologies)
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.:
- Weber, N. C., 1984. "On resampling techniques for regression models," Statistics & Probability Letters, Elsevier, vol. 2(5), pages 275-278, October.
- Joel L. Horowitz, 1996. "Bootstrap Methods in Econometrics: Theory and Numerical Performance," Econometrics 9602009, EconWPA, revised 05 Mar 1996.
- 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, . "REGRESET: RATS procedure to perform Ramsey RESET test on regression," Statistical Software Components RTS00181, Boston College Department of Economics.
- Tom Doan, . "REGWHITENNTEST: RATS procedure to perform White neural network test on regression," Statistical Software Components RTS00183, Boston College Department of Economics.
- Horowitz, Joel L., 1994. "Bootstrap-based critical values for the information matrix test," Journal of Econometrics, Elsevier, vol. 61(2), pages 395-411, April.
- 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.
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.