Exploring the experience-expectation nexus in macroeconomic forecasting using computational text analysis and machine learning (renewal application to "macroeconomic forecasting in great crisis")

使用计算文本分析和机器学习探索宏观经济预测中的经验预期关系（更新应用于“大危机中的宏观经济预测”）

• 批准号: 275693836
• 负责人: Professor Dr. Jörg Döpke
• 金额: --
• 依托单位: Fachgebiet Volkswirtschaftslehre
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 财政年份: 2015
• 资助国家: 德国
• 起止时间: 2014-12-31 至 2022-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/275693836?language=en
• 关键词: Exploring; experience; expectation; nexus; macroeconomic

Based on the findings of the first project period and using the quantitative and qualitative data collected so far in the first work package we will use methods from corpus linguistics (sentiment analysis, topic models) to investigate the experience-expectation nexus and the role of ideology for the production of macroeconomic forecasts. This includes several channels: the selection and assessment of incoming information, the possible strategic usage of forecasts to enforce economic policy reaction, and the impact of the mentioned aspects on the formation of textually expressed expectations.In the second work package we will make use of several results of the corpus-linguistic analysis to investigate aspects of forecast optimality using non-linear methods and machine learning tools, and we will use machine-learning tools to improve the computer-linguistic analysis further.In the third work package we will analyse several other aspects and research gaps in the experience-expectation nexus relevant for macroeconomic forecasts which all explicitly call for the combination of qualitative and quantitative data.The borders between the work packages are not sharp. We expect to realize strong synergy effects between the work packages and through discussions with other related projects of the Priority Program.

基于第一个项目期间的发现，并使用到目前为止在第一个工作包中收集的定量和定性数据，我们将使用语料库语言学的方法（情感分析，主题模型）来调查经验-期望关系以及意识形态在宏观经济预测生产中的作用。这包括几个渠道：选择和评估传入的信息，可能战略性地使用预测来执行经济政策反应，以及上述方面对形成文本表达的预期的影响。在第二个工作包中，我们将利用语料库语言分析的几个结果来研究使用非线性方法和机器学习工具的预测最优性方面，我们将使用机器学习工具进一步改进计算机语言分析。在第三个工作包中，我们将分析与宏观经济预测相关的经验-期望关系中的其他几个方面和研究差距，这些方面都明确要求将定性和定量数据结合起来。工作包之间的边界并不明显。我们期待在工作包之间以及通过与优先计划其他相关项目的讨论，实现强大的协同效应。