Carpe diem: Can daily oil prices improve model-based forecasts of the real price of crude oil?
提出利用每日名义价格信息改进月度平均实际油价预测的方法，发现相比现有模型预测误差可减半，且首次证明短期预测能优于随机游走模型。
This paper proposes methods to include information from the underlying nominal daily series in model-based forecasts of average real series. We apply these methods to forecasts of the real price of crude oil. Models utilizing information from daily prices yield large forecast improvements and, in some cases, almost halve the forecast error compared to current specifications. We demonstrate for the first time that model-based forecasts of the real price of crude oil can outperform the traditional random walk forecast, that is, the end-of-month no-change forecast, at short forecast horizons.