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A radial basis function artificial neural network test for ARCH

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  • Blake, Andrew P.
  • Kapetanios, George

Abstract

We 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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  • 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.
    • Handle: RePEc:eee:ecolet:v:69:y:2000:i:1:p:15-23
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    1. 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.
    2. Arup Bose, 1990. "Bootstrap in moving average models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 42(4), pages 753-768, December.
    3. Anne Peguin-Feissolle, 1999. "A comparison of the power of some tests for conditional heteroscedasticity," Post-Print halshs-00390157, HAL.
    4. 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.
    5. Bera, Anil K & Higgins, Matthew L, 1993. "ARCH Models: Properties, Estimation and Testing," Journal of Economic Surveys, Wiley Blackwell, vol. 7(4), pages 305-366, December.
    6. Bollerslev, Tim & Chou, Ray Y. & Kroner, Kenneth F., 1992. "ARCH modeling in finance : A review of the theory and empirical evidence," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 5-59.
    7. Peguin-Feissolle, Anne, 1999. "A comparison of the power of some tests for conditional heteroscedasticity," Economics Letters, Elsevier, vol. 63(1), pages 5-17, April.
    8. 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.
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    Citations

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    Cited by:

    1. Benigno, Pierpaolo & Woodford, Michael, 2012. "Linear-quadratic approximation of optimal policy problems," Journal of Economic Theory, Elsevier, vol. 147(1), pages 1-42.
    2. Hong, Seung Hyun & Phillips, Peter C. B., 2010. "Testing Linearity in Cointegrating Relations With an Application to Purchasing Power Parity," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(1), pages 96-114.
    3. Kanazawa, Nobuyuki, 2020. "Radial basis functions neural networks for nonlinear time series analysis and time-varying effects of supply shocks," Journal of Macroeconomics, Elsevier, vol. 64(C).
    4. Di Bartolomeo, Giovanni & Di Pietro, Marco & Giannini, Bianca, 2016. "Optimal monetary policy in a New Keynesian model with heterogeneous expectations," Journal of Economic Dynamics and Control, Elsevier, vol. 73(C), pages 373-387.
    5. Andrew P. Blake & George Kapetanios, 2003. "Pure Significance Tests of the Unit Root Hypothesis Against Nonlinear Alternatives," Journal of Time Series Analysis, Wiley Blackwell, vol. 24(3), pages 253-267, May.
    6. Blake, Andrew P. & Kapetanios, George, 2007. "Testing for ARCH in the presence of nonlinearity of unknown form in the conditional mean," Journal of Econometrics, Elsevier, vol. 137(2), pages 472-488, April.
    7. Brendon, Charles & Ellison, Martin, 2018. "Time-consistently undominated policies," LSE Research Online Documents on Economics 87176, London School of Economics and Political Science, LSE Library.
    8. Paez-Farrell, Juan, 2011. "Timeless perspective versus discretionary policymaking when the degree of inflation persistence is unknown," Economic Modelling, Elsevier, vol. 28(6), pages 2432-2438.
    9. Blake, Andrew P. & Kirsanova, Tatiana, 2004. "A note on timeless perspective policy design," Economics Letters, Elsevier, vol. 85(1), pages 9-16, October.
    10. Andrew P. Blake & George Kapetanios, 2007. "Testing for Neglected Nonlinearity in Cointegrating Relationships," Journal of Time Series Analysis, Wiley Blackwell, vol. 28(6), pages 807-826, November.
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    12. 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: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 63(2), pages 769-787, September.
    13. George Kapetanios & Andrew P. Blake, 2007. "Testing the Martingale Difference Hypothesis Using Neural Network Approximations," Working Papers 601, Queen Mary University of London, School of Economics and Finance.
    14. Jensen, Christian, 2016. "Discretion Rather than Rules? Binding Commitments versus Discretionary Policymaking," MPRA Paper 76838, University Library of Munich, Germany.
    15. George A. Waters, 2015. "Careful Price Level Targeting," International Symposia in Economic Theory and Econometrics, in: William A. Barnett & Fredj Jawadi (ed.), Monetary Policy in the Context of the Financial Crisis: New Challenges and Lessons, volume 24, pages 29-40, Emerald Publishing Ltd.
    16. Anesti, Nikoleta & Kalamara, Eleni & Kapetanios, George, 2021. "Forecasting UK GDP growth with large survey panels," Bank of England working papers 923, Bank of England.
    17. George Kapetanios & Andrew P. Blake, 2007. "Boosting Estimation of RBF Neural Networks for Dependent Data," Working Papers 588, Queen Mary University of London, School of Economics and Finance.
    18. Lee Tae-Hwy, 2001. "Neural Network Test and Nonparametric Kernel Test for Neglected Nonlinearity in Regression Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 4(4), pages 1-15, January.
    19. Gradojevic, Nikola & Kukolj, Dragan & Adcock, Robert & Djakovic, Vladimir, 2023. "Forecasting Bitcoin with technical analysis: A not-so-random forest?," International Journal of Forecasting, Elsevier, vol. 39(1), pages 1-17.
    20. Thierry Warin & Aleksandar Stojkov, 2021. "Machine Learning in Finance: A Metadata-Based Systematic Review of the Literature," JRFM, MDPI, vol. 14(7), pages 1-31, July.

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