Judgmental forecasting with time series and causal information

Although contextual or causal information has been emphasised in forecasting, few empirical studies have been conducted on this issue in controlled conditions. This study investigates the way people adjust statistical forecasts in the light of contextual/causal information. Results indicate that people appeared to reasonably incorporate extra-model causal information to make up for what the statistical time-series model lacks. As expected, the effectiveness of causal adjustment was contingent upon the reliability of the causal information. While adjustment of forecasts using causal information of low reliability did not lead to significant improvement, adjustment using highly reliable causal information produced forecasts more accurate than the best statistical models. However, people relied too heavily on their initial forecasts compared with the optimal model. Moreover, people did not seem to learn over time to modify this conservative behaviour. People also seemed to prefer statistical forecasts in favour of causal information.