Perhaps the most widely followed price in the market is the price of crude oil. The volatility of this commodity is evident to consumers through the gasoline prices that consumers see on the retail side. The U.S. Energy Information Agency provides widely followed forecasts for the retail gasoline price (along with other energy products) produced with their short-term energy outlook (STEO) model. The purpose of this research is to compare a number of forecasts using different techniques to the STEO model. This is accomplished through the use of Holt Winters, structural, ARIMA, and vector error-correction models. We also construct a composite forecast by averaging the respective forecasts from the four models. From the empirical analysis, we find evidence from the structural model and the vector error-correction model that the movement in the gasoline prices can be explained by the West Texas Intermediate (WTI) benchmark and the spread between BRENT and WTI benchmarks. In terms of forecasting performance, the additive Holt Winters model outperforms the other models within sample. Out sample, the composite forecast is the best performing model. The composite forecast has a MAPE of 6.3% versus a MAPE of 8.1% from the STEO model.