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Neural Network Approach to Estimating Conditional Quantile Polynomial Distributed Lag (QPDL) Model with an Application to Rubber Price Returns
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Volume 3, 2015
Issue 3 (June)
Pages: 162-170   |   Vol. 3, No. 3, June 2015   |   Follow on         
Paper in PDF Downloads: 27   Since Aug. 28, 2015 Views: 1714   Since Aug. 28, 2015
Kwadwo Agyei Nyantakyi, Ghana Institute of Management and Public Administration (GIMPA), Greenhill College, Business School, Achimota-Accra, Ghana.
In this paper we consider the estimation of the conditional quantile polynomial distributed lag (QPDL) using neural network to investigate the influence of the conditioning variables on the location, scale and shape parameters of the QPDL model developed. This method avoids the need for a distributional assumption and applies conditional quantiles approach which allows the investigator to employ a range of conditional functions which exposes a variety of forms of conditional heterogeneity to give a more comprehensive picture of the effects of the independent variable on the dependent variable. The models fitted were adequate with very high R-square and low AIC values across the quantiles. We observe the effects of the quantiles on the dependent variables through the various GAM-style plots. Also from the actual and the predicted plots we observed that there was no difference between them. The results suggest that neural network used in estimating the QPDL model offers a useful alternative for estimating the conditional density, as artificial neural networks have proven to produce good prediction results in regression problems.
Backpropagation, Effects, Feedforward, Hidden Neurons, Polynomial Transformation
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