International Oil Price Forecasting Model

Oil is the most basically raw material of modern industry and the lifeline of entire economical development. The fluctuation of the oil price and the change of the economics are closely linked, the three historical oil shocks causes comprehensive decline of world economics and all kinds of social crises. The international oil price in the last few years continues to rise and it shocks in the largest scale, as well as it has brought various influences to economic growth of various countries. The oil price rises suddenly and falls suddenly in July, 2008, it causes that the government, the enterprise, the expert and the investor are all at a loss. The impact of the oil price reduces the economic growth rate of the oil importing countries, and it even reduces the absolute output level that it causes the economic recession; it drives up prices level and it possibly causes the inflation. Obviously, we research the development's regularity of the oil price, and forecast dynamically the future international oil price with this rule. Its significance will be unusual.First the background of the international oil price, the common forecasting technique are have studied, and five major influencing factor about the international oil price are have induced in this article. And the strangeness analysis to the oil price sequence is carried on, the time section is divided into two types: time interval of including singular point; time interval of not including singular point. It is evaluated about the forecast effect of delay dependent variable regressive model, (AR-R) double-regressive model, (AR) neural network model, lag 1 step neural network model in the different time interval, which the model is simple and its effect is also ideal is lag 1 step neural network model with the two time intervals. And, (AR-R) double-regressive model is a new improved model on the traditional multiple regressive model, it is confirmed that the effect of (AR-R)-double regressive model surpasses the effect of traditional multiple regressive model by concrete example.In addition, the model's fitting and forecasting procedure is compiled with SAS and MATLAB in this article, the analysis and the research work about the example has been completed, and the feasibility of the following model has been proved by the empirical study and the contrastive analysis on their results, the beneficial attempt to seek more ideal forecasting technique is made.1. Delay dependent variable regressive model. It is a random analysis method of nonstationary series, the data quantity is few, its operation is easy and feasible, the explanation of the model is easy and its effect is the best in the several monad nonstationary series in the two time intervals by comparison.2. (AR-R) double-regressive model. The improvement of the method lies in the decision of the forecasting factor. It has been joined in the historical data of the dependent variable on the forecasting factor, the several delay dependent variable series which coefficient of autocorrelation is the biggest retain; the factor which his movement is at the same time with dependent variable in the choice of external influencing factor retains.3. (AR) neural network model. The difference between it and the traditional neural network is that it embodys the inertia of the sequence's selfmovement, it is more suitable to forecast on the not including singular point time interval.4. Lag 1 step neural network model. This model manifested that other factors are earlier sensitively to the change of the economic environment than the oil price, the rise and drop of oil price is forecasted extremely accurate, and this model is quite suitable to the forecast of oil price is confirmed by the concrete example.Finally, the conclusion is drawn as follows:The fitting and forecasting is made by different time interval is the best, the effect is the best when dependent variable factor should better not to be as independent variable factor on including singular point time interval and but the effect is the best when dependent variable factor should better to be as independent variable factor on not including singular point time interval. The effect of lag 1 step neural network is the best and its error may also is accepted as to the omen of the forecasting and the difficulty of the data gathering on the two different time interval.The effect of (AR) neural network model is the best on not including singular point time interval, the effect of tradition neural network model is the best on including singular point time interval, speaking of the forecasting precision; The effect of (AR-R) double-regressive model is the best on not including singular point time interval, speaking of explanatory ability of the model; The rise and drop of oil price may be judged by the above model, which the model is simple and the effect is also ideal is lag 1 step neural network.Each model has different merit and shortcoming respectively, which kind of model is used to make forecasting that must be according to the situation or generalized analysis is made according to their results.