Forecasting Macroeconomic Dynamics using a Calibrated Data-Driven Agent-based Model

Marco Pangallo, François Lafond, J. Doyne Farmer, Samuel Wiese, John Muellbauer, José Moran, Joel Dyer, Jagoda Kaszowska-Mojsa (jagoda.kaszowska-mojsa@maths.ox.ac.uk) and Anisoara Calinescu (ani.calinescu@cs.ox.ac.uk)
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Jagoda Kaszowska-Mojsa: Institute for New Economic Thinking, University of Oxford
Anisoara Calinescu: Institute for New Economic Thinking, University of Oxford

INET Oxford Working Papers from Institute for New Economic Thinking at the Oxford Martin School, University of Oxford

Abstract: In the last few years, economic agent-based models have made the transition from qualitative models calibrated to match stylised facts to quantitative models for time series forecasting, and in some cases, their predictions have performed as well or better than those of standard models (see, e.g. Poledna et al. (2023a); Hommes et al. (2022); Pichler et al. (2022)). Here, we build on the model of Poledna et al., adding several new features such as housing markets, realistic synthetic populations of individuals with income, wealth and consumption heterogeneity, enhanced behavioural rules and market mechanisms, and an enhanced credit market. We calibrate our model for all 38 OECD member countries using state-of-the-art approximate Bayesian inference methods and test it by making out-of-sample forecasts. It outperforms both the Poledna and AR(1) time series models by a highly statistically significant margin. Our model is built within a platform we have developed, making it easy to build, run, and evaluate alternative models, which we hope will encourage future work in this area.

Keywords: Agent-based models; Bayesian estimation; Economic forecasting (search for similar items in EconPapers)
Pages: 73 pages
Date: 2024-09
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