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A Radial Basis Function Artificial Neural Network Test for Neglected Nonlinearity

Author

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  • Andrew Blake
Abstract
We propose a test for neglected nonlinearity that uses an artificial neural network. We use radial basis functions for the `hidden layer' with basis function centers and radii chosen from the sample data set and selected on the basis of information criteria. The procedure is straightforward to implement and out-performs the random network test proposed by Lee, White and Granger (1993).

Suggested Citation

  • Andrew Blake, 1999. "A Radial Basis Function Artificial Neural Network Test for Neglected Nonlinearity," National Institute of Economic and Social Research (NIESR) Discussion Papers 153, National Institute of Economic and Social Research.
    • Handle: RePEc:nsr:niesrd:153
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    Cited by:

    1. 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.
    2. 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).
    3. Marian Vavra, 2013. "Testing for linear and Markov switching DSGE models," Working and Discussion Papers WP 3/2013, Research Department, National Bank of Slovakia.
    4. Isao Ishida & Virmantas Kvedaras, 2015. "Modeling Autoregressive Processes with Moving-Quantiles-Implied Nonlinearity," Econometrics, MDPI, vol. 3(1), pages 1-53, January.
    5. Psaradakis, Zacharias & Vávra, Marián, 2014. "On testing for nonlinearity in multivariate time series," Economics Letters, Elsevier, vol. 125(1), pages 1-4.
    6. Baillie, Richard T. & Kapetanios, George, 2007. "Testing for Neglected Nonlinearity in Long-Memory Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 447-461, October.
    7. Marian Vavra, 2012. "Robustness of Power Properties of Non-linearity Tests," Birkbeck Working Papers in Economics and Finance 1205, Birkbeck, Department of Economics, Mathematics & Statistics.
    8. Konstantinos N. Konstantakis & Panagiotis T. Cheilas & Ioannis G. Melissaropoulos & Panos Xidonas & Panayotis G. Michaelides, 2023. "Supply chains and fake news: a novel input–output neural network approach for the US food sector," Annals of Operations Research, Springer, vol. 327(2), pages 779-794, August.

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