Using judgment to select and adjust forecasts from statistical models

Forecasting support systems allow users to choose different statistical forecasting methods. But how well do they make this choice? We examine this in two experiments. In the first one (N = 191), people selected the model that they judged to perform the best. Their choice outperformed forecasts made by averaging the model outputs and improved with a larger difference in quality between models and a lower level of noise in the data series. In a second experiment (N = 161), participants were asked to make a forecast and were then offered advice in the form of a model forecast. They could then re-adjust their forecast. Final forecasts were more influenced by models that made better forecasts. As forecasters gained experience, they followed input from high-quality models more readily. Thus, both experiments show that forecasters have ability to use and learn from visual records of past performance to select and adjust model-based forecasts appropriately.