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

TL;DR

This paper develops a comprehensive, calibrated agent-based macroeconomic model with new features like housing markets and heterogeneous agents, demonstrating superior out-of-sample forecasting performance across OECD countries.

Contribution

It introduces an enhanced, data-driven agent-based model with new market features and calibrates it for all OECD countries, outperforming existing models in forecasts.

Findings

Model outperforms Poledna et al. and AR(1) models significantly.

Incorporates realistic housing markets and heterogeneous agents.

Provides a flexible platform for future macroeconomic modeling.

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.