Econometric Analysis of Dynamic Games with Limited Information

本项目将提供在动态市场中实证研究企业行为所需的计量经济学方法，在动态市场中，企业可能对周围环境的信息有限，信息处理能力各异，就像在数字市场中发生的那样，并为这一领域的政策提供信息。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情况，任何其他相关的过去和现在的市场特征，以及这些特征在概率方面的未来演变。然而，当企业在现代在线市场中运营时，这种完美假设变得不切实际。通过在复杂、分层、不断变化和模块化的环境中托管多个利益相关者，亚马逊和eBay等在线市场无可避免地增加了公司必须获得的信息量，以确保完全知情。根据市场经验以及存储和分析数据的复杂程度，一些公司可能对市场活动有全面的了解;其他公司可能需要时间来适应、反应和实验，这也需要人工智能学习算法的帮助。在处理和使用数字平台上可访问的大量信息时出现的这种摩擦构成了竞争监管挑战，因为它们阻止我们应用标准的计量经济学方法来估计公司的收入和成本，而这对于研究市场力量和反垄断案件至关重要。反过来，我们对企业在线市场激励机制的了解主要局限于描述性证据，不足以指导政策。因此，发展计量经济学方法来处理有限信息的动态博弈是一个至关重要的问题，为认真的实证研究铺平道路，这将是本项目的重点。新方法的严格理论将得到发展。新方法的性能将在模拟研究中进行检查，其实施将与真实的市场数据进行说明。将公布开放源代码，使从业人员能够直接采用新工具。除了学者，这些从业者包括政府，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产业组织的学者感兴趣，他们将受益于对以前不存在的现代动态环境进行实证研究的方法。此外，这一项目将是第一个为决策者提供适当工具的项目，以分析市场力量，发现反竞争做法，并保护在线市场的消费者。新方法还将帮助数字平台增强对用户动机的了解，这对于设计保护收入和其他关键利益的平台规则至关重要。

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.