The price of oil affects everyone, everyday. This thesis investigates two different approaches of oil price models for prediction and forecasting. The first is an structural model and is an application of the Stock-Watson method. This model is extended to a structural systematic model, applying the Johansen method. The second approach is a series of univariate time series models starting with ARIMA models, followed by an artificial neural network regression model. All the methods and models are justified with corresponding econometric tests and procedures. The results are very conclusive, as they confirm what was shown in earlier research on different time periods. It is confirmed that in terms of forecasting the nonlinear models perform the best, however the structural models help explaining effects and importance of single variables.