WHAT CENTRAL BANKERS NEED TO KNOW ABOUT FORECASTING OIL PRICES
研究了如何为央行构建准确的季度油价预测，发现基于月度数据的向量自回归模型优于其他方法，包括期货价格和无变化预测。
Central banks routinely use short‐horizon forecasts of the quarterly price of oil in assessing the global and domestic economic outlook. We address a number of econometric issues specific to the construction of quarterly oil price forecasts in the United States and abroad. We show that quarterly forecasts of the real price of oil from suitably designed vector autoregressive models estimated on monthly data generate the most accurate real‐time forecasts overall among a wide range of methods, including quarterly averages of forecasts based on monthly oil futures prices, no‐change forecasts, and forecasts based on regression models estimated on quarterly data.