Economic forecasting with an agent-based model

Poledna, S., Miess, M.G., Hommes, C., & Rabitsch, K. (2022). Economic forecasting with an agent-based model. European Economic Review 151 e104306. 10.1016/j.euroecorev.2022.104306.

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Abstract

We develop the first agent-based model (ABM) that can compete with benchmark VAR and DSGE models in out-of-sample forecasting of macro variables. Our ABM for a small open economy uses micro and macro data from national accounts, sector accounts, input–output tables, government statistics, and census and business demography data. The model incorporates all economic activities as classified by the European System of Accounts (ESA 2010) and includes all economic sectors populated with millions of heterogeneous agents. In addition to being a competitive model framework for forecasts of aggregate variables, the detailed structure of the ABM allows for a breakdown into sector-level forecasts. Using this detailed structure, we demonstrate the ABM by forecasting the medium-run macroeconomic effects of lockdown measures taken in Austria to combat the COVID-19 pandemic. Potential applications of the model include stress-testing and predicting the effects of monetary or fiscal macroeconomic policies.

Item Type: Article
Research Programs: Advancing Systems Analysis (ASA)
Advancing Systems Analysis (ASA) > Exploratory Modeling of Human-natural Systems (EM)
Depositing User: Luke Kirwan
Date Deposited: 02 Nov 2022 15:58
Last Modified: 15 Nov 2022 10:10
URI: https://pure.iiasa.ac.at/18339

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