European Companies Search Engine
UK funding (£255,232): Econometric Analysis of Dynamic Games with Limited Information Ukri1 Feb 2024 UK Research and Innovation, United Kingdom
Overview
Text
Econometric Analysis of Dynamic Games with Limited Information
| Abstract | 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. |
| Category | Research Grant |
| Reference | ES/X011186/1 |
| Status | Active |
| Funded period start | 01/02/2024 |
| Funded period end | 31/01/2028 |
| Funded value | £255,232.00 |
| Source | https://gtr.ukri.org/projects?ref=ES%2FX011186%2F1 |
Participating Organisations
| Queen Mary University of London |
The filing refers to a past date, and does not necessarily reflect the current state. The current state is available on the following page: Queen Mary University of London, London.
The visualizations for "Queen Mary University of London - UK funding (£255,232): Econometric Analysis of Dynamic Games with Limited Information"
are provided by
North Data
and may be reused under the terms of the
Creative Commons CC-BY license.
