Data-Driven Economic Agent-Based Models

2024/12/21 by Marco Pangallo, R. Maria del Rio-Chanona, Pangallo, Marco +1 · 2 voices · 2 citations
#econ.GN

paper · pdf · doi:10.48550/arxiv.2412.16591

Abstract

Economic agent-based models (ABMs) are becoming more and more data-driven, establishing themselves as increasingly valuable tools for economic research and policymaking. We propose to classify the extent to which an ABM is data-driven based on whether agent-level quantities are initialized from real-world micro-data and whether the ABM's dynamics track empirical time series. This paper discusses how making ABMs data-driven helps overcome limitations of traditional ABMs and makes ABMs a stronger alternative to equilibrium models. We review state-of-the-art methods in parameter calibration, initialization, and data assimilation, and then present successful applications that have generated new scientific knowledge and informed policy decisions. This paper serves as a manifesto for data-driven ABMs, introducing a definition and classification and outlining the state of the field, and as a guide for those new to the field.

Cited by

• Comparing Data Assimilation and Likelihood-Based Inference on Latent State Estimation in Agent-Based Models
• Prospects of Imitating Trading Agents in the Stock Market

Discussions

• 🚨 New paper alert! Economic Agent Based Models have become increasingly data-driven. What does that mean and what impact can this have? arxiv.org/abs/2412.16591 📖✨ Chapter with @marcopangallo.b [bsky, 19 points, 1 comments]
• Agent based models are also quantitative and have been used in policy too, arxiv.org/abs/2412.16591 I mean I don't think they will solve everything, I'm just saying they are underappreciated and add [bsky, 2 points, 1 comments]

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