Forecasting Coke's Price by Combination Semi-Parametric Regression Model

Forecasting Coke's Price by Combination Semi-Parametric Regression Model

Jiaojiao Li, Linfeng Zhao
Copyright: © 2022 |Pages: 14
DOI: 10.4018/IRMJ.308302
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Abstract

According to the characteristics of consumption of coke, the article utilized combination semi-parametric regression method rather than usual regression analysis to forecast the coke's price. Coke' price was divided into two parts. Parameter part was analyzed through error correction model and BP artificial neural network. Nonlinear part was fitted by core function. Nonparametric sector was described by coke yield. The article utilized cross validation method to select optimum bandwidth, choosing the Parabola kernel for the kernel function. The least squares estimation was selected in new model estimation. The estimation results of real case demonstrate that error correction-semi-parametric regression model and BP artificial neural network-semi-parametric regression model not only reduced boundary estimation error but also strengthen economical interpretation. It is an effective method to forecast coke's price, which can largely raise the estimation precision of coke's price.
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Semi-Parameter—An Introduction To The Error Correction Algorithm

Semi-Parameter—Specification of Error Correction Model

Semi-Parameter—the Equation (1) for error correction regression model:Yi=IRMJ.308302.m01+ g(Xi) +IRMJ.308302.m02(1) where Y is taken as a variable, IRMJ.308302.m03 is error correction operator, g(Xi) is the equation for unknown functions, IRMJ.308302.m04 (i=1, …, n) is the random error sequence with a zero-mean value (Wong, C.M, et al. 1996). For the Equation (1) associations a parametric model and a non-parametric model, and the parameters come in error correction sequence, so it can also be called semi-parametric—error correction model.

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