Leah Zitter
经济学家很难预测石油的价格，因为他们的波动受各种各样因素的影响。专家依靠一系列的工具预测石油价格并依靠时间确认或者推翻他们的预测。最常使用的五个模型是石油期货价格、回归结构模型、时间序列分析、贝叶斯自回归模型和动态随机一般均衡图。由于经济学家还没有确定哪一种模型最可靠，他们使用这些模型的加权组合以得到最准确的答案。
石油期货价格
中央银行和国际货币基金组织在判断时主要使用石油期货价格。期货价格是卖方同意在未来特定时间将一定量的石油以特定价格卖给买方的价格。交易者根据两个因素估计石油期货价格，即供求关系和市场情绪。供求关系是指交易者对石油供应和未来市场需求的判断。市场情绪是指交易者对未来石油价格上涨或者下跌的判断。石油期货价格的预测效果非常有限，因为他们通常给当前的石油价格增加太多变数。
回归结构模型
统计计算机程序计算石油价格变动的概率。例如，数学家可能考虑石油输出国组织、石油库存水平、生产成本或者石油产量和消费等的影响。回归结构模型有很强的预测能力，但是科学家可能漏掉一个或者多个因素或者出现意外导致模型失败。
时间序列模型
一些经济学家使用例如指数平滑模型和自回归模型的时间序列模型来修正石油期货价格的限制。这些模型分析石油在各个时间点上的历史以提取有意义的数据并根据先前的观察预测未来价格。时间序列分析有时会出现错误，但是在更短的时间范围内能提供更准确的结果。
贝叶斯向量自回归模型
统计计算机程序使用贝叶斯模型计算某些已经预测事件对石油的影响。科学家使用标准的回归模型并试图通过增加有影响事件的可能变化因素的计算进行改进。大多数当代科学家喜欢使用贝叶斯向量自回归模型预测石油价格，2015年的国际货币基金组织工作报告指出这些模型在18个月时间段中表现最好。贝叶斯向量自回归模型在2008到2009年和2014到2015年间准确预测了石油价格。
动态随机一般均衡模型
动态随机一般均衡模型使用宏观经济原则来解释复杂的经济现象，在本文中是石油价格。动态随机一般均衡模型有时候会有效，但是他们的成功要依赖事件和政策保持不变，因为动态随机一般均衡模型的计算是基于历史观察。
这些模型的结合
当专家想要预测原油的价格，他们会使用所有模型的加权组合，因为没有一个单一的模型能提供准确的预测。例如在2014年，欧洲央行使用了一个四个模型的组合预测石油价格，产生了一个相当准确的结果。当然，欧洲央行也使用更少或者更多的模型获得最好的结果。每个数学模型都有时效性。政治不稳定、生产成本或者自然灾害等不可预测因素将会影响计算。正是因为这个因素，一些模型在一定的时间段内能比其他的有效。
How Do Professionals Forecast Crude Oil Prices?
By Leah Zitter
Economists are hard-pressed to predict oil prices since they are volatile and depend on various situations. Experts use a range of forecasting tools to predict oil prices and depend on time to confirm or disprove their predictions. The five models used most often are oil futures prices, regression-based structural models, time-series analysis, Bayesian autoregressive models and dynamic stochastic general equilibrium graphs. Because economists are still undecided as to which method is most reliable, they use a weighted combination of them all to get the most accurate answer.
Oil Futures Prices
Central banks and the International Monetary Fund (IMF) mainly use oil futures prices as their gauge. Futures prices are used when traders create oil futures contracts where the seller agrees to sell a certain number of barrels of oil to the purchaser at a predetermined price on a predetermined date. The trader estimates the crude oil futures price by two factors: supply and demand and market sentiment. Supply and demand refers to the trader’s speculations on oil supply and the future market demand for that oil. Sentiment refers to the trader’s speculations in the increase, or decrease, of the future price of oil. Oil futures prices can be a poor predictor of the price of oil because they tend to add too much variance to the current price of oil.
Regression-Based Structural Models
Statistical computer programming calculates the probabilities of certain behaviors on the price of oil. For instance, mathematicians may consider forces such as behavior among members of the Organization of Petroleum Exporting Countries (OPEC), oil inventory levels, production costs, or oil consumption and production. Regression-based models have strong predictive power, but scientists may fail to include one or more factors, or unexpected variables may step in to cause these regression-based models to fail.
Time-Series Models
Some economists use time-series models such as exponential smoothing models and autoregressive models, that include the categories of ARIMA and the ARCH/GARCH, to correct for the limitations of oil futures prices. These models analyze the history of oil at various points in time to extract meaningful statistics and predict future values based on previously observed values. Time-series analysis sometimes errs but usually produces more accurate results when economists apply it to shorter time spans.
Bayesian Vector Autoregressive Model
Statistical computer programs use Bayesian methods to calculate the probability of the impact of certain predicted events on oil. Mathematicians use the standard regression-based model and try to improve upon it by adding calculations of possible change factors to the impacting events. Most contemporary economists like to use the Bayesian vector autoregressive (BVAR) model for predicting oil prices, although a 2015 International Monetary Fund Working Paper noted these models work best when used on a maximum 18-month horizon and when a smaller number of predictive variables are inserted. BVAR models accurately predicted the price of oil during the years 2008-2009 and 2014-2015.
Dynamic Stochastic General Equilibrium Model
Dynamic stochastic general equilibrium (DSGE) models use macroeconomic principles to explain complex economic phenomena; in this case, prices of oil. DSGE models sometimes work, but their success depends on events and policies remaining unchanged, since DSGE calculations are based on historical observations.
Combining the Models
When experts want to predict the price of crude oil, they use a weighted combination of all the models since no one model alone offers an accurate prediction. In 2014, for instance, the European Central Bank (ECB) used a four-model combination to predict oil prices to generate a more accurate forecast. There have been times, however, when the ECB has used fewer or more models to capture best results. Each mathematical model is time-dependent. Unforeseen factors that may alter the calculations include political instability, production costs or natural disasters. It is for this reason that some models work better at one time than another.
本文翻译由兄弟财经提供
文章来源：http://www.investopedia.com/articles/investing/041516/how-are-crude-oil-prices-forecast-professionals.asp