Machine Learning in Macroeconomic Modeling

宏观经济建模中的机器学习

• 批准号: 1824253
• 负责人: In-Koo Cho
• 金额: $ 31.1万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-15 至 2019-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1824253&HistoricalAwards=false
• 关键词: Machine; Learning; Macroeconomic; Modeling

This award funds research that will examine whether incorporating machine learning algorithms into macroeconomic models can result in better ways to understand modern economies. The project will begin by assuming that individual consumers, workers, and firms can be modeled as making their decisions according to a particular type of machine learning algorithm (a boosting algorithm). The project seeks to understand the conditions under which a machine learning algorithm can emulate the decision-making process of a rational individual. The team will also analyze likely long run economic outcomes when these algorithms are used under various institutional and informational assumptions. The project, therefore, may develop a valuable new technique for modeling individual decisions in the context of an entire economic system. It could also help us understand how future economic outcomes may be affected by the increased use of machine learning methods to aid or even substitute for human decision making. The project could therefore help guide efforts to improve the competitiveness of the US economy.The research team will exploit one of the central components of the machine learning algorithm, called the boosting algorithm, to build a highly accurate forecasting rule from a collection of rudimentary and possibly inaccurate forecasting rules. The usual approach in economic models is to assume that the agents (individuals, firms, etc.) are typically endowed with misspecified models. When this is the case, an individual or firm's decision-making process typically relies on simple, yet well fit, forecasting rules, which can differ from the true data generating process. The team aim to understand whether an agent endowed with flawed but well fit models can behave as if she knows the true data generating process. The team plans to pursue this objective in two steps. In the first part of the project, the team will examine learning dynamics under misspecified models. As an example, it examines a new class of learning models in which the agent has to learn the growth rate instead of the level of a variable of interest. In many macroeconomic models, the growth rate (e.g., inflation rate) rather than the level of a variable (e.g., price) is the main focus of the investigation. Assuming that the agent learns the growth rate through a recursive learning process rather than rational expectations may result in better explanations of important macroeconomic dynamics, such as recurrent hyperinflation and stock price volatility. The second step in the research plan investigates the dynamics of a specific machine learning algorithm with two research objectives: [1] if an agent is endowed with misspecified models, how the decision maker can test and build a new model to improve the forecast, and [2] what are the asymptotic properties of the processes of constructing new models, in particular whether the agent can emulate the rational agent in the long run.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.

该奖项资助的研究将研究将机器学习算法纳入宏观经济模型是否可以更好地理解现代经济。 该项目将开始假设个人消费者，工人和公司可以根据特定类型的机器学习算法（提升算法）进行决策。该项目旨在了解机器学习算法可以模拟理性个体决策过程的条件。该团队还将分析在各种制度和信息假设下使用这些算法时可能的长期经济结果。因此，该项目可能会开发一种有价值的新技术，用于在整个经济系统的背景下对个人决策进行建模。它还可以帮助我们了解未来的经济结果可能会受到越来越多地使用机器学习方法来帮助甚至取代人类决策的影响。因此，该项目可能有助于指导提高美国经济竞争力的努力。研究团队将利用机器学习算法的核心组件之一，即所谓的提升算法，从一组基本的、可能不准确的预测规则中构建一个高度准确的预测规则。经济模型中的通常方法是假设代理人（个人，公司等）通常被赋予错误的模型。在这种情况下，个人或公司的决策过程通常依赖于简单但非常适合的预测规则，这些规则可能与真正的数据生成过程不同。该团队的目标是了解一个被赋予有缺陷但拟合良好的模型的代理是否可以表现得好像她知道真正的数据生成过程。 该小组计划分两步实现这一目标。在项目的第一部分，团队将研究在错误指定的模型下的学习动态。 作为一个例子，它研究了一类新的学习模型，其中代理必须学习增长率，而不是感兴趣的变量的水平。在许多宏观经济模型中，增长率（例如，通货膨胀率）而不是变量的水平（例如，价格）是调查的主要焦点。 假设代理人通过递归学习过程而不是理性预期来学习增长率，可能会更好地解释重要的宏观经济动态，例如经常性的恶性通货膨胀和股票价格波动。研究计划的第二步是研究特定机器学习算法的动态，有两个研究目标：[1]如果代理被赋予错误指定的模型，决策者如何测试和构建新模型以改进预测，[2]构建新模型的过程的渐近性质是什么，该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。