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Nonlinear Economic Models : Cross Sectional Times Series And Neural Network Applications
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1 Aug 2018 . Keywords: Forecasting; Nonlinear time series; Neural networks; Moving . follows: in sections 2 and 3 present the nonlinear MA model,. and the . congurations of the network, under the cross-validation . asvirta T. Forecasting economic variables with nonlinear models. . foundations and applications.
9 Oct 1997 . Nonlinear Economic Models: Cross-Sectional, Time Series and Neural . artificial neural network and genetic algorithm models of financial markets. . range of applications in micro and macroeconomics, labour and finance.
It is rare to find studies that concentrate on forecasting nonlinear times series that . This leads to the importance of formulating new models of neural networks such as . in sections 2 Nonlinear moving average model, 3 Neural networks models . series and different configurations of the network, under the cross-validation.
The value of neural network models in forecasting economic time series has been . Hu (1998), with recent applications including Swanson and White (1997a, 1997b), . the data, while Section 3 and reports the results of nonlinearity tests. . by including p(p+1)/2 cross-product terms at different lag lengths in the regression.
China has achieved remarkable success in economic development by . Section 4 presents the neural network model for time series forecasting. Section . works application to macroeconomics time series forecasting is quite novel. NEURAL . generally nonlinear operation on the results, and then outputs the final result.
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