A Radial Basis Function Artificial Neural Network Test for ARCH
AbstractWe propose a test for ARCH that uses a radial basis function artificial neural network. It outperforms alternative neural network tests in a variety of Monte Carlo experiments.
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Bibliographic InfoPaper provided by National Institute of Economic and Social Research in its series NIESR Discussion Papers with number 188.
Date of creation: Sep 1999
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- Blake, Andrew P. & Kapetanios, George, 2000. "A radial basis function artificial neural network test for ARCH," Economics Letters, Elsevier, vol. 69(1), pages 15-23, October.
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- Davidson, Russell & MacKinnon, James G, 1998.
"Graphical Methods for Investigating the Size and Power of Hypothesis Tests,"
The Manchester School of Economic & Social Studies,
University of Manchester, vol. 66(1), pages 1-26, January.
- Russell Davidson & James G. MacKinnon, 1994. "Graphical Methods for Investigating the Size and Power of Hypothesis Tests," Working Papers 903, Queen's University, Department of Economics.
- Peguin-Feissolle, Anne, 1999.
"A comparison of the power of some tests for conditional heteroscedasticity,"
Elsevier, vol. 63(1), pages 5-17, April.
- 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.
- Bera, Anil K & Higgins, Matthew L, 1993. " ARCH Models: Properties, Estimation and Testing," Journal of Economic Surveys, Wiley Blackwell, vol. 7(4), pages 305-66, December.
- Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
- 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.
- Arup Bose, 1990. "Bootstrap in moving average models," Annals of the Institute of Statistical Mathematics, Springer, vol. 42(4), pages 753-768, December.
- Juan Paez-Farrell, 2009. "Timeless perspective vs discretionary policymaking when the degree of inflation persistence is unknown," Discussion Paper Series 2009_14, Department of Economics, Loughborough University, revised Sep 2009.
- Yen-Ming Chiang & Wei-Guo Cheng & Fi-John Chang, 2012. "A hybrid artificial neural network-based agri-economic model for predicting typhoon-induced losses," Natural Hazards, International Society for the Prevention and Mitigation of Natural Hazards, vol. 63(2), pages 769-787, September.
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