Leah Zitter
經濟學家很難預測石油的價格，因為他們的波動受各種各樣因素的影響。專家依靠一系列的工具預測石油價格並依靠時間確認或者推翻他們的預測。最常使用的五個模型是石油期貨價格、回歸結構模型、時間序列分析、貝葉斯自回歸模型和動態隨機一般均衡圖。由於經濟學家還沒有確定哪一種模型最可靠，他們使用這些模型的加權組合以得到最準確的答案。
石油期貨價格
中央銀行和國際貨幣基金組織在判斷時主要使用石油期貨價格。期貨價格是賣方同意在未來特定時間將一定量的石油以特定價格賣給買方的價格。交易者根據兩個因素估計石油期貨價格，即供求關系和市場情緒。供求關系是指交易者對石油供應和未來市場需求的判斷。市場情緒是指交易者對未來石油價格上漲或者下跌的判斷。石油期貨價格的預測效果非常有限，因為他們通常給當前的石油價格增加太多變數。
回歸結構模型
統計計算機程序計算石油價格變動的概率。例如，數學家可能考慮石油輸出國組織、石油庫存水平、生產成本或者石油產量和消費等的影響。回歸結構模型有很強的預測能力，但是科學家可能漏掉一個或者多個因素或者出現意外導致模型失敗。
時間序列模型
一些經濟學家使用例如指數平滑模型和自回歸模型的時間序列模型來修正石油期貨價格的限制。這些模型分析石油在各個時間點上的歷史以提取有意義的數據並根據先前的觀察預測未來價格。時間序列分析有時會出現錯誤，但是在更短的時間範圍内能提供更準確的結果。
貝葉斯向量自回歸模型
統計計算機程序使用貝葉斯模型計算某些已經預測事件對石油的影響。科學家使用標準的回歸模型並試圖通過增加有影響事件的可能變化因素的計算進行改進。大多數當代科學家喜歡使用貝葉斯向量自回歸模型預測石油價格，2015年的國際貨幣基金組織工作報告指出這些模型在18個月時間段中表現最好。貝葉斯向量自回歸模型在2008到2009年和2014到2015年間準確預測了石油價格。
動態隨機一般均衡模型
動態隨機一般均衡模型使用宏觀經濟原則來解釋複雜的經濟現象，在本文中是石油價格。動態隨機一般均衡模型有時候會有效，但是他們的成功要依賴事件和政策保持不變，因為動態隨機一般均衡模型的計算是基於歷史觀察。
這些模型的結合
當專家想要預測原油的價格，他們會使用所有模型的加權組合，因為沒有一個單一的模型能提供準確的預測。例如在2014年，歐洲央行使用了一個四個模型的組合預測石油價格，產生了一個相當準確的結果。當然，歐洲央行也使用更少或者更多的模型獲得最好的結果。每個數學模型都有時效性。政治不穩定、生產成本或者自然災害等不可預測因素將會影響計算。正是因為這個因素，一些模型在一定的時間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.
本文翻譯由兄弟財經提供
文章來源：http://www.investopedia.com/articles/investing/041516/how-are-crude-oil-prices-forecast-professionals.asp