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Using judgment to select and adjust forecasts from statistical models
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DOI：10.1016/j.ejor.2020.01.028.png)
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摘要
En 中文
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. Crown Copyright (C) 2020 Published by Elsevier B.V. All rights reserved.
Keyword:
Forecasting
Judgmental selection
Judgmental adjustment
Forecast support systems
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期刊
IF:
6
论文数:
2.2W
被引数:
6.4W
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