Radial basis functions for multivariable interpolation: a review
Algorithms for approximation
Neural network performance on the bankruptcy classification problem
Proceedings of the 15th annual conference on Computers and industrial engineering
Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Backpropagation: the basic theory
Backpropagation
Forecasting exchange rates using general regression neural networks
Computers and Operations Research - Neural networks in business
Stock Market Prediction with Backpropagation Networks
IEA/AIE '92 Proceedings of the 5th international conference on Industrial and engineering applications of artificial intelligence and expert systems
Expert Systems with Applications: An International Journal
Fast learning in networks of locally-tuned processing units
Neural Computation
Expert Systems with Applications: An International Journal
IEEE Transactions on Neural Networks
Diagonal recurrent neural networks for dynamic systems control
IEEE Transactions on Neural Networks
| Hi-index | 12.05 |
The study aims to propose a family of Neural Networks (NN) model to achieve improvement in modeling nonlinear cointegration compared to Hansen and Seo (2002) Threshold Autoregressive Vector Error Correction (TAR-VEC) model. Our proposed TAR-VEC-NN family consist of TAR-VEC Multi Layer Perceptron (TAR-VEC-MLP), TAR-VEC Radial Basis Function (TAR-VEC-RBF) and TAR-VEC Recurrent Hybrid Elman (TAR-VEC-RHE) models. TAR-VEC-NN models are also discussed under two modeling strategies, first based on TAR-VEC modeling and the second based on a NN modeling approaches. The TAR-VEC-NN models proposed are analyzed for modeling monthly returns of TL/$ real exchange rate and ISE100 Istanbul Stock Exchange Index. For the data analyzed in the study, the TAR-VEC-NN models and their nonlinear cointegration structure improve forecast accuracy compared to TAR-VEC models; for both modeling strategies, we obtained similar results. Even though TAR-VEC-MLP model provides comparatively significant forecast improvement, TAR-VEC-RHE and TAR-VEC-RBF models achieve better forecast accuracy as expected given the dynamic memory structure of RHE and given the basis functions of RBF models which capture nonlinear error correction more efficiently. Further, our results show that, though with in sample accuracy, TAR-VEC-MLP and TAR-VEC-RHE produced the low RMSE values, in terms of long run predictions, the RBF model produced best results which is expected given the basis functions' capability in capturing deviations with the gaussian functions in a nonlinear error correction system. Thus, in the literature the forecasting ability of VEC type models are commonly criticized. With the use of our approach, there is an important improvement in VEC based models with NN specifications in terms of forecasts which cannot be disregarded.