Oil Price Forecasting Using FRED Data: A Comparison between Some Alternative Models

This paper investigates the forecasting accuracy of alternative time series models when augmented with partial least-squares (PLS) components extracted from economic data, such as Federal Reserve Economic Data, well Monthly Database (FRED-MD). Our results indicate that PLS FRED-MD data reduce error linear models, ARIMA and SARIMA, but produce poor forecasts during high-volatility periods. In contrast, conditional variance ARCH GARCH, more accurate regardless whether or not are used.