Surveying Professional Forecasters

Y. Grushka-Cockayne, K. C. Lichtendahl
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Abstract

This case illustrates how averaging point forecasts harnesses the wisdom of crowds. Students access data from the Survey of Professional Forecasters (SPF) and compare the performance of the crowd (i.e., the average point forecasts) to the average performance of the individual panelists and the best performer from the previous period.The case is intended for use in a class on forecasting, and the instructor can present it in three ways: with all necessary SPF data cleaned and preprocessed in a student spreadsheet (UVA-QA-0805X, provided with the case); with code (also provided in the student spreadsheet) written by the case authors in R, the statistical computing package, as well as a supplementary handout (UVA-QA-0805H, also provided with the case), which walks students through R code, explaining how to clean and analyze the SPF data; or as a team project to be worked on over several days, providing neither the spreadsheet nor the supplement. Excerpt UVA-QA-0805 Rev. Apr. 7, 2014 SURVEYING PROFESSIONAL FORECASTERS Since 1981, the Wall Street Journal surveyed economists for point forecasts of economic indicators such as gross domestic product (GDP), inflation, and unemployment. These forecasts were scored and ranked annually based on their accuracy. The top-ranked forecasters were celebrated in a special announcement of the complete ranking on the Journal's website, typically followed by a press release by the top forecasters' employers. In 2012, the Journal decided for the first time to average all the forecasts for each indicator and present that set of forecasts as an additional panelist. The economists' average forecast, or “the crowd,” was then ranked alongside the actual panelists. How had the economists' average forecast performed? How did the crowd compare to “chasing the expert”? Among the 49 panelists, it ranked 12th in 2012. Yet Don Leavens and Tim Gill, the top-ranked team in 2011, came in 5th in 2012 (see Exhibits 1 and 2). What did this say about the wisdom of the crowd? . . .
测量专业预报员
这个案例说明了平均点预测如何利用群体的智慧。学生们从专业预测者调查(SPF)中获取数据,并将人群的表现(即平均点预测)与个别小组成员的平均表现以及前一时期表现最佳的表现进行比较。该案例旨在用于预测课程,讲师可以通过三种方式呈现:将所有必要的SPF数据在学生电子表格中清洗和预处理(UVA-QA-0805X,随案例提供);案例作者用R语言编写的代码(也在学生电子表格中提供),统计计算包,以及一份补充讲义(UVA-QA-0805H,也在案例中提供),该讲义引导学生学习R代码,解释如何清理和分析SPF数据;或者作为一个团队项目,需要在几天内完成,既不提供电子表格也不提供补充。自1981年以来,《华尔街日报》对经济学家进行调查,对国内生产总值(GDP)、通货膨胀和失业率等经济指标进行点预测。这些预测每年都会根据其准确性进行评分和排名。排名靠前的预测者将在《华尔街日报》网站上以特别公告的形式公布完整的排名,之后通常会由这些预测者的雇主发布新闻稿。2012年,《华尔街日报》首次决定对每个指标的所有预测取平均值,并将这组预测作为一个额外的小组成员提出。经济学家的平均预测,或“人群”,然后被排在实际小组成员的旁边。经济学家的平均预测结果如何?群众如何比较“追逐专家”?在49个小组成员中,2012年排名第12位。然而,2011年排名第一的唐·利文斯(Don Leavens)和蒂姆·吉尔(Tim Gill)在2012年仅排在第五位(见表1和表2)。这说明了人群的智慧是什么?……
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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