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We develop the first agent-based model (ABM) that can compete with and in the long run significantly outperform 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 sector accounts, input-output tables, government statistics, and census data. The model incorporates all economic activities as classified by the European System of Accounts as heterogeneous agents. The detailed structure of the ABM allows for a breakdown into sector level forecasts. Potential applications of the model include stress-testing and predicting the effects of monetary or fiscal macroeconomic policies.

AbstractCascading risks that can spread through complex systems have recently gained attention. As it is crucial for decision‐makers to put figures on such risks and their interactions, models that explicitly capture such interactions in a realistic manner are needed. Climate related hazards often cascade through different systems, from physical to economic and social systems, causing direct but also indirect risks and losses. Despite their growing importance in the light of ongoing climate change and increasing global connections, such indirect risks are not well understood. Applying two fundamentally different economic models—a computable general equilibrium model and an agent‐based model—we reveal indirect risks of flood events. The models are fed with sector‐specific capital stock damages, which constitutes a major methodological improvement. We apply these models for Austria, a highly flood exposed country with strong economic linkages. A key finding is that flood damages pose very different indirect risks to different sectors and household groups (distributional effects) in the short and long‐term. Our results imply that risk management should focus on specific societal subgroups and sectors. We provide a simple metric for indirect risk, showing how direct and indirect losses are related. This can provide new ways forward in risk management, for example, focusing on interconnectedness of sectors and agents within different risk‐layers of indirect risk. Although we offer highly relevant leverage points for indirect risk management in Austria, the methodology of analyzing indirect risks can be transferred to other regions.

Reliable estimates of indirect economic losses arising from natural disasters are currently out of scientific reach. To address this problem, we propose a novel approach that combines a probabilistic physical damage catastrophe model with a new generation of macroeconomic agent-based models (ABMs). The ABM moves beyond the state of the art by exploiting large data sets from detailed national accounts, census data, and business information, etc., to simulate interactions of millions of agents representing \backslashemph{each} natural person or legal entity in a national economy. The catastrophe model introduces a copula approach to assess flood losses, considering spatial dependencies of the flood hazard. These loss estimates are used in a damage scenario generator that provides input for the ABM, which then estimates indirect economic losses due to the event. For the first time, we are able to link environmental and economic processes in a computer simulation at this level of detail. We show that moderate disasters induce comparably small but positive short- to medium-term, and negative long-term economic impacts. Large-scale events, however, trigger a pronounced negative economic response immediately after the event and in the long term, while exhibiting a temporary short- to medium-term economic boost. We identify winners and losers in different economic sectors, including the fiscal consequences for the government. We quantify the critical disaster size beyond which the resilience of an economy to rebuild reaches its limits. Our results might be relevant for the management of the consequences of systemic events due to climate change and other disasters.