Econometric Analysis of Dynamic Games with Limited Information

有限信息动态博弈的计量经济学分析

• 批准号: ES/X011186/1
• 负责人: Cristina Gualdani
• 金额: $ 32.52万
• 依托单位: Queen Mary University of London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=ES%2FX011186%2F1
• 关键词: Econometric; Analysis; Dynamic; Games; Limited

This project will provide the econometric methods needed to empirically study firms' behaviour in dynamic markets where firms may have limited information about the surrounding environment and heterogeneous information-processing capabilities, as happens in digital markets, and inform policy in this area.The available econometric methods to empirically study firms' behaviour in dynamic markets require firms to access and absorb a large amount of information. Firms need to know competitors' characteristics and past actions, what competitors observe at each point in time, any other relevant past and present market features, and the future evolution of such features in probabilistic terms. However, this perfection assumption becomes unrealistically demanding when firms operate in modern online markets. By hosting multiple stakeholders in intricate, layered, constantly changing, and modular environments, online markets such as Amazon and eBay have incommensurably increased the amount of information firms must acquire to act as perfectly informed. Depending on the experience in the market and the sophistication in storing and analysing data, some firms may have a full view of the market activities; others may need time to adapt, react, and experiment, also helped by artificial intelligence learning algorithms. Such frictions in processing and using the sheer volume of information accessible on digital platforms pose competition regulatory challenges because they prevent us from applying standard econometric methods to estimate firms' revenues and costs, which are essential for studying market power and antitrust cases. In turn, our knowledge of firms' incentives in online markets is mainly confined to descriptive evidence and is insufficient to guide policy. Developing econometric methods to handle dynamic games with limited information is, therefore, an issue of utmost importance to pave the way for serious empirical studies and will be the focus of this project. A rigorous theory for the new methods will be developed. The properties of the new methods will be examined in simulation studies, and their implementation will be illustrated with real market data. Open-source codes will be published, making the adoption of the new tools straightforward for practitioners. In addition to academics, these practitioners include professional economists in governments, public agencies, and the private sector. The research findings will interest scholars in econometrics and industrial organisation, who will benefit from having methods to empirically study modern dynamic environments that did not exist before. Further, this project will be the first to give policymakers appropriate tools to analyse market power, detect anti-competitive practices, and protect consumers in online markets. The new methods will also help digital platforms enhance their knowledge of users' incentives, which is crucial to designing platform rules that safeguard revenues and other key interests.

该项目将提供经验研究公司在动态市场中的行为所需的计量经济学方法，在该市场中，企业可能像在数字市场中发生的那样，有关周围环境和异构信息处理能力的信息有限，并在该领域的政策上依据。企业需要了解竞争对手的特征和过去的行动，竞争对手在每个时间点观察到的内容，任何其他相关的过去和现在市场特征以及以概率术语的未来发展。但是，当公司在现代在线市场中运营时，这种完美的假设变得不切实际。通过在复杂，分层，不断变化和模块化环境中托管多个利益相关者，亚马逊和eBay等在线市场不得不增加信息公司所必须获得的信息量，以充分了解。根据市场上的经验以及存储和分析数据的复杂性，一些公司可能对市场活动有全面的看法。其他人可能需要时间来适应，反应和实验，这也是人工智能学习算法的帮助。在处理和使用可在数字平台上访问的大量信息方面进行的这种摩擦提出了监管挑战，因为它们使我们无法应用标准的计量经济学方法来估算公司的收入和成本，这对于研究市场权力和反污染案例至关重要。反过来，我们对企业在在线市场中的激励措施的了解主要仅限于描述性证据，不足以指导政策。因此，发展有限信息的动态游戏的发展计算方法是为认真的经验研究铺平道路的最重要问题，并且将成为该项目的重点。将开发针对新方法的严格理论。新方法的属性将在模拟研究中进行检查，并将使用实际市场数据来说明它们的实施。开源代码将发布，使从业人员可以简单地采用新工具。除学者外，这些从业人员还包括政府，公共机构和私营部门的专业经济学家。研究结果将使计量经济学和工业组织中的学者感兴趣，他们将受益于拥有以前不存在的现代动态环境的方法。此外，该项目将是第一个为决策者提供适当的工具来分析市场能力，检测反竞争实践并保护在线市场中的消费者的项目。新方法还将帮助数字平台增强他们对用户激励措施的了解，这对于设计平台规则至关重要，以保护收入和其他关键利益。