全球智库信息集成服务系统(G2TT): Expert Forecasting with and without Uncertainty Quantification and Weighting: What Do the Data Say?

G2TT

来源类型	Journal Article in International Journal of Forecasting
规范类型	论文
	Expert Forecasting with and without Uncertainty Quantification and Weighting: What Do the Data Say?
	Roger Cooke; Deniz Marti; Thomas Mazzuchi
发表日期	2020-07-27
出处	International Journal of Forecasting
出版年	2020
语种	英语
摘要	Post-2006 expert judgment data has been extended to 530 experts assessing 580 calibration variables from their fields. New analysis shows that point predictions as medians of combined expert distributions outperform combined medians, and medians of performance weighted combinations outperform medians of equal weighted combinations. Relative to the equal weight combination of medians, using the medians of performance weighted combinations yields a 65% improvement. Using the medians of equally weighted combinations yields a 46% improvement. The Random Expert Hypothesis underlying all performance-blind combination schemes, namely that differences in expert performance reflect random stressors and not persistent properties of the experts, is tested by randomly scrambling expert panels. Generating distributions for a full set of performance metrics, the hypotheses that the original panels’ performance measures are drawn from distributions produced by random scrambling are rejected at significance levels ranging from E−6 to E−12. Random stressors cannot produce the variations in performance seen in the original panels. In- and out-of-sample validation results are updated.
主题	Risk Analysis and Uncertainty
URL	https://www.rff.org/publications/journal-articles/expert-forecasting-and-without-uncertainty-quantification-and-weighting-what-do-data-say/
来源智库	Resources for the Future (United States)
资源类型	智库出版物
条目标识符	http://119.78.100.153/handle/2XGU8XDN/588335
推荐引用方式 GB/T 7714	Roger Cooke,Deniz Marti,Thomas Mazzuchi. Expert Forecasting with and without Uncertainty Quantification and Weighting: What Do the Data Say?. 2020.