A reliable forecast is needed that can be done accurately via somewhat sophisticated techniques, such as autoregressive (integrated) moving average cause effect (AR(I)MAX), rather than the simple cause-effect regression technique. The cause-effect regression technique does not recover lagged systematic effects or unexpected changes for an accurate forecast, but an AR(I)MAX model includes (a) autoregressive filters to account for systematic effects and (b) moving average filters to account for shock effects in itself in addition to explanatory variables in the cause-effect regression model. Therefore, the AR(I)MAX technique is able to outperform the simple cause-effect technique in terms of forecast accuracies (Akal, 2004).