HNDS-R Collaborative Research: Measuring Belief Distortions to Improve Predictive Outcomes

嵌入信念中的系统性预期错误（信念扭曲）会对经济产生重要影响。本项目旨在探讨信念扭曲与经济结果之间的关系。这项研究将探讨与货币政策密切相关的关键变量（如利率和金融总量）的信念可能被扭曲到何种程度。该项目还将研究投票中表达的信念和情绪，例如与选举有关的投票，如何产生对选举结果的有偏见的预测。在探索这些问题的过程中，该项目将利用来自在线新闻媒体和社交媒体等书面文件的文本数据，以及来自博彩市场的数据，这些数据将进一步与其他经济指标相结合。通过利用这些数据，该项目将解释受访者对未来货币政策路径的预期偏差，以及民意调查和调查在预测金融市场变量和选举结果方面的表现。该项目将开发基于机器学习的方法，通过发现调查回答中的系统错误，并使用人工智能算法纠正这些错误，来改进在一系列依赖调查的环境中的预测和估计。这些工具将为各种经济背景下的调查预测准确性提供相当大的潜在改进。解决信念是否有偏见的一个基本挑战是，不存在对这种扭曲的客观衡量。本研究旨在通过利用机器学习的进步来解决这一挑战。本项目所采用的方法的一般前提是，大数据算法可以有效地用于揭示多种情况下人类判断中的主观偏见，从而促进更准确的客观预测。该项目将构建和研究一种全面的、方法上一致的计量经济学方法，以衡量人们对未来货币政策和选举结果以及其他变量的预期中的信念扭曲。这一目标需要使用与实时决策和机器学习工具相关的大量数据来减少采样噪声。从书面文件中抓取的数据将使用潜在狄利克雷分配类型的模型进行分析，以提取新闻媒体和社交媒体所涵盖主题的高频度量。有关选举结果、博彩市场和金融市场期货合约的数据将被添加到一个大型的实时经济信息数据集中。然后，研究将结合这些方法和数据来研究与未来货币政策行为、金融市场行为和选举结果相关的调查预期中发现的信念和可能的判断错误。从期权和期货市场获取的快速变化的变量，以及从在线文件中抓取的变量，将使高频跟踪信念和客观预测的修正成为可能。该框架将允许将信念的高频修正与特定的新闻和公告联系起来，以提高预测的准确性。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Systematic expectational errors embedded in beliefs (belief distortions) can have important effects on the economy. This project aims to explore the relation between belief distortions and economic outcomes. The research will explore to what extent beliefs pertaining to key variables closely related to monetary policy, such as interest rates and financial aggregates, may be distorted. The project will also examine how the beliefs and sentiments voiced in polling, such as those pertaining to elections, may produce biased forecasts of election outcomes. In exploring these questions, the project will utilize textual data from written documents such as online news outlets and social media, and data from betting markets, which will further be combined with other economic indicators. By utilizing this data, the project will account for biases in surveyed expectations about the future path of monetary policy as well as the performance of polls and surveys in predicting financial market variables and election outcomes. The project will develop machine learning based methods to improve prediction and estimation in a range of settings that rely on surveys by uncovering systematic errors in survey responses, and by correcting these errors using artificial intelligence algorithms. These tools will provide considerable potential improvements in prediction accuracy of surveys in a variety of contexts in the economy.A fundamental challenge in addressing whether beliefs are biased is that no objective measure of such distortions exists. This research aims to address this challenge by leveraging advancements in machine learning. A general premise of the approach followed in this project is that big data algorithms can be productively employed to reveal subjective biases in human judgements in multiple contexts, thereby facilitating more accurate objective forecasts. The project will construct and study a comprehensive, methodologically consistent, econometric measure of belief distortions in expectations about future monetary policy and electoral outcomes, among other variables. This objective requires employment of large amounts of data related to real-time decision making and machine-learning tools to reduce sampling noise. Data scraped from written documents will be analyzed using a Latent Dirichlet Allocation type of model to extract high-frequency measures of the topics covered by news outlets and social media. Data about election outcomes, betting markets, and financial market futures contracts will be added to a large real time dataset of economic information. The research will then incorporate these methodologies and data to study beliefs and possible judgmental errors found in survey expectations related to the future conduct of monetary policy, the behavior of financial markets, and the outcomes of elections. The inclusion of fast-moving variables taken from options and futures markets and scraped from online documents will enable high-frequency tracking of revisions in beliefs and objective forecasts. This framework will allow high-frequency revisions in beliefs to be connected to specific news and announcements to improve prediction accuracy.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.