Project description
More accurate, realistic models to understand economic booms and busts
Understanding why developed economies face rare but severe economic crises remains crucial for macroeconomics. Current models that assume agents have rational expectations are too complex to effectively handle these scenarios. The ERC-funded HA-FRONTIER project will help replace the rational expectation assumption with a more realistic approach. This will be inspired by reinforcement learning, specifically temporal difference learning from psychology and computer science. The proposed method will model how agents form expectations about future prices based on empirical evidence such as survey data. HA-FRONTIER will help enable the development of a new macroeconomic theory that explains crises, including asset-price booms and crashes, offering valuable insights into economic stability and risk.
Objective
One of the key developments in macroeconomics research over the last three decades has been the incorporation of explicit heterogeneity into models of the macroeconomy. But one of the all-time biggest questions in macroeconomics has so far been out of reach for this approach: why do developed economies experience infrequent but large economic crises? This is due to a problematic feature of current heterogeneous agent (HA) models: the assumption of rational expectations means that agents forecast prices by forecasting distributions resulting in the entire cross-sectional distribution entering their decision problem. This extreme version of the curse of dimensionality hugely increases the models’ computational complexity.
HA-FRONTIER aims to overcome this problem by developing alternative approaches to the rational expectations assumption. I will adapt ideas from the literature on reinforcement learning in psychology and computer science (temporal difference learning) to better model how agents form expectations about future prices. I will incorporate key empirical evidence on expectations formation, e.g. from the large literature on survey expectations. Departing from rational expectations thus “kills two birds with one stone”: it makes HA models with aggregate risk operational while, at the same time, making them more realistic. Finally, I will put this methodology to use to develop an empirically grounded macroeconomic theory of infrequent but large economic crises that incorporates asset-price booms driven by individuals reallocating their portfolios toward bubbly assets and crashes driven by the reverse movement.
To disseminate the ideas developed by HA-FRONTIER, I will write a PhD-level textbook on heterogeneous-agent macroeconomics. Such a textbook is currently missing, thereby creating unnecessary entry barriers for junior researchers, practitioners, and policy makers.
Fields of science (EuroSciVoc)
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CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
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Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.1 - European Research Council (ERC)
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Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
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Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
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Call for proposal
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(opens in new window) ERC-2024-ADG
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WC2A 2AE London
United Kingdom
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