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Neural Networks to Detect Nonlinearities in Time Series: Analysis of Business Cycle in France and the United Kingdom

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  • Kiani, K.M.

    ()

Abstract

In this research we investigate possible existence of nonlinearities in business cycle fluctuations in France and United Kingdom (U.K.) real gross domestic product (GDP). We model the relationship between the real GDP in these countries using neural network linearity tests via in-sample as well as jackknife out-of-sample testing. Our results based on neural network linearity tests for possible existence of nonlinearities due to Terasvirta el al. (1993) using in-sample forecasts from neural nets in France and U.K. show statistically significant evidence of nonlinearities in both the series. Similarly, the results on linearity tests based on jackknife out-of-sample forecast also show statistically significant evidence of nonlinearities in both France and U.K. series. Moreover, the results based on neural network test for neglected nonlinearities that was proposed by Lee el al. (1993) also show statistically significant evidence of nonlinearities in both the countries. Therefore, policymakers are not able to evaluate the impact of monetary policy or any other shocks on output in these countries that are based on predictions from linear models.

Suggested Citation

  • Kiani, K.M., 2009. "Neural Networks to Detect Nonlinearities in Time Series: Analysis of Business Cycle in France and the United Kingdom," Applied Econometrics and International Development, Euro-American Association of Economic Development, vol. 9(1).
  • Handle: RePEc:eaa:aeinde:v:9:y:2009:i:1_7
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    References listed on IDEAS

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    1. Prasad V. Bidarkota, 2000. "Asymmetries in the Conditional Mean Dynamics of Real GNP: Robust Evidence," The Review of Economics and Statistics, MIT Press, vol. 82(1), pages 153-157, February.
    2. Allan D. Brunner, 1997. "On The Dynamic Properties Of Asymmetric Models Of Real GNP," The Review of Economics and Statistics, MIT Press, vol. 79(2), pages 321-352, May.
    3. Beaudry, Paul & Koop, Gary, 1993. "Do recessions permanently change output?," Journal of Monetary Economics, Elsevier, vol. 31(2), pages 149-163, April.
    4. Hutchinson, James M & Lo, Andrew W & Poggio, Tomaso, 1994. " A Nonparametric Approach to Pricing and Hedging Derivative Securities via Learning Networks," Journal of Finance, American Finance Association, vol. 49(3), pages 851-889, July.
    5. Norman R. Swanson & Halbert White, 1997. "A Model Selection Approach To Real-Time Macroeconomic Forecasting Using Linear Models And Artificial Neural Networks," The Review of Economics and Statistics, MIT Press, vol. 79(4), pages 540-550, November.
    6. Sichel, Daniel E, 1989. "Are Business Cycles Asymmetric? A Correction," Journal of Political Economy, University of Chicago Press, vol. 97(5), pages 1255-1260, October.
    7. Garcia, Rene & Gencay, Ramazan, 2000. "Pricing and hedging derivative securities with neural networks and a homogeneity hint," Journal of Econometrics, Elsevier, vol. 94(1-2), pages 93-115.
    8. Maria Simona Andreano & Giovanni Savio, 2002. "Further evidence on business cycle asymmetries in G7 countries," Applied Economics, Taylor & Francis Journals, vol. 34(7), pages 895-904.
    9. 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.
    10. Anderson, Heather M. & Vahid, Farshid, 1998. "Testing multiple equation systems for common nonlinear components," Journal of Econometrics, Elsevier, vol. 84(1), pages 1-36, May.
    11. Khurshid M. Kiani & Prasad V. Bidarkota & Terry L. Kastens, 2005. "Forecast performance of neural networks and business cycle asymmetries," Applied Financial Economics Letters, Taylor and Francis Journals, vol. 1(4), pages 205-210, July.
    12. Khurshid M. Kiani & Prasad V. Bidarkota, 2004. "On Business Cycle Asymmetries in G7 Countries," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 66(3), pages 333-351, July.
    13. Neftci, Salih N, 1984. "Are Economic Time Series Asymmetric over the Business Cycle?," Journal of Political Economy, University of Chicago Press, vol. 92(2), pages 307-328, April.
    14. Gencay, Ramazan, 1999. "Linear, non-linear and essential foreign exchange rate prediction with simple technical trading rules," Journal of International Economics, Elsevier, vol. 47(1), pages 91-107, February.
    15. Swanson, Norman R & White, Halbert, 1995. "A Model-Selection Approach to Assessing the Information in the Term Structure Using Linear Models and Artificial Neural Networks," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 265-275, July.
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    More about this item

    Keywords

    asymmetries; neural networks; nonlinearities; principal components; real GDP;

    JEL classification:

    • B22 - Schools of Economic Thought and Methodology - - History of Economic Thought since 1925 - - - Macroeconomics
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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