 Using judgment to select and adjust forecasts from statistical modelsShari De Baets and Nigel Harvey European Journal of Operational Research, 2020, vol. 284, issue 3, 882-895 Abstract: 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. Keywords: Forecasting; Judgmental selection; Judgmental adjustment; Forecast support systems (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:284:y:2020:i:3:p:882-895 DOI: 10.1016/j.ejor.2020.01.028 Access Statistics for this article European Journal of Operational Research is currently edited by Roman Slowinski, Jesus Artalejo, Jean-Charles. Billaut, Robert Dyson and Lorenzo Peccati More articles in European Journal of Operational Research from Elsevier |
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