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Scenarios of the Romanian GDP Evolution With Neural Models

Author

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  • Saman, Corina

    () (Institute for Economic Forecasting, Centre for Macroeconomic Modeling, NIER, Romanian Academy.)

Abstract

This paper aims to explore the nonlinear relation between investments and GDP. The method of neural network is used to construct two nonlinear models of GDP in relation to domestic investments, foreign direct investments and real interest rate. The results show that the two neural models present good performance measures on the dataset. The improved forecast accuracy may be capturing more fundamental non-linearities between investment and financial variables and the real output for a longer horizon.

Suggested Citation

  • Saman, Corina, 2011. "Scenarios of the Romanian GDP Evolution With Neural Models," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 129-140, December.
  • Handle: RePEc:rjr:romjef:v::y:2011:i:4:p:129-140
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    File URL: http://www.ipe.ro/rjef/rjef4_11/rjef4_2011p129-140.pdf
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    References listed on IDEAS

    as
    1. Ghysels, Eric & Granger, Clive W J & Siklos, Pierre L, 1996. "Is Seasonal Adjustment a Linear or Nonlinear Data-Filtering Process?," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(3), pages 374-386, July.
    2. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    3. 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.
    4. Zeira, Joseph, 1990. "Cost uncertainty and the rate of investment," Journal of Economic Dynamics and Control, Elsevier, vol. 14(1), pages 53-63, February.
    5. 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.
    6. Tkacz, Greg, 2001. "Neural network forecasting of Canadian GDP growth," International Journal of Forecasting, Elsevier, vol. 17(1), pages 57-69.
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    Citations

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    Cited by:

    1. Andrei, Dalina Maria, 2012. "Foreign Direct Investments in Romania. A Structural and Dynamic View," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 129-146, December.
    2. Chih-Chung Yang & Yungho Leu & Chien-Pang Lee, 2014. "A Dynamic Weighted Distancedbased Fuzzy Time Series Neural Network with Bootstrap Model for Option Price Forecasting," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 115-129, June.

    More about this item

    Keywords

    investment; simulation; GDP; neural networks;

    JEL classification:

    • E22 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Investment; Capital; Intangible Capital; Capacity
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

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