Econometrics for the Firm (FIRMMETRIX)

企业计量经济学 (FIRMMETRIX)

• 批准号: EP/X02931X/1
• 负责人: Aureo De Paula
• 金额: $ 182.32万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FX02931X%2F1
• 关键词: Econometrics; Firm; FIRMMETRIX

The research this proposal describes will tackle existing challenges with the empirical assessment of firms' production processes and interactions. Firms' decisions and interactions lie in the background of the current debate on important themes such as inequality, market power, productivity, and the origins of macroeconomic fluctuations. The quantification of the main drivers of firms' behaviour is a key ingredient to many metrics colouring those debates. The identification of the causal links between firms' inputs and outputs and their market behaviour is thus fundamental as imprecisions here will steer conclusions on those big societal themes. Yet, (a) modern procedures to estimate production possibilities from conventional data rest on critical assumptions on how and when firms choose inputs that may unreliably drive estimates; (b) important multi-firm negotiation models lack clear guidelines on the data requirements for their empirical validity; and (c) systematic market definition protocols - key for research and competition policy - remain a challenge. This proposal offers ground-breaking research addressing these issues.The proposal incorporates new and increasingly available data to address the issues above; develops the necessary tools to take on the econometric challenges that these new data bring; and shows through substantive applications how this approach helps. The proposal covers methodological considerations and empirical applications on the analysis of firm interaction models in four workstreams that complement and build on one another. The workstreams will: (i) incorporate data on firm expectations and business networks to address the issues in (a) in two separate workstreams; (ii) formally explore the data demands for the accurate assessment of firm-to-firm relationship models noted in (b); (iii) and bring together machine learning methods and economics for market segmentation to address (c).

本提案所述的研究将通过对企业生产流程和相互作用的经验评估来应对现有挑战。企业的决策和相互作用是当前关于不平等、市场力量、生产率和宏观经济波动根源等重要主题的辩论的背景。对企业行为的主要驱动因素进行量化是影响这些辩论的许多衡量标准的关键因素。因此，确定企业的投入和产出与其市场行为之间的因果关系至关重要，因为这里的不精确性将引导对这些重大社会主题的结论。然而，（a）根据传统数据估计生产可能性的现代程序依赖于关于公司如何和何时选择可能不可靠地推动估计的投入的关键假设;（B）重要的多公司谈判模式缺乏关于其经验有效性的数据要求的明确准则;（c）系统的市场定义协议-研究和竞争政策的关键-仍然是一项挑战。该提案为解决这些问题提供了突破性的研究，并纳入了新的和越来越多的可用数据来解决上述问题;开发了必要的工具来应对这些新数据带来的计量经济学挑战;并通过实质性应用显示了这种方法如何提供帮助。该提案涵盖了在相互补充和相互借鉴的四个工作流程中分析公司互动模式的方法考虑和经验应用。工作流程将：（i）纳入关于公司预期和商业网络的数据，以在两个不同的工作流程中解决（a）中的问题;（ii）正式探讨准确评估（B）中提到的公司对公司关系模型的数据需求;（iii）将机器学习方法和市场细分经济学结合起来，以解决（c）。