Analyzing and forecasting economic crises with an agent-based model of the euro area - Cars Hommes; Sebastian Poledna (Tinbergen Institute, 2023)
We develop an agent-based model for the euro area that fulfils widely recommended requirements for nextgeneration macroeconomic models by i) incorporating financial frictions, ii) relaxing the requirement of rational expectations, and iii) including heterogeneous agents. Using macroeconomic and sectoral data, the model includes all sectors (financial, non-financial, household, and a general government) and connects financial flows and balance sheets with stock-flow consistency. The model, moreover, incorporates many features considered essential for future policy models, such as a financial accelerator with debt-financed investment and a complete GDP identity, and allows for non-linear responses. We first show that the agent-based model outperforms dynamic stochastic general equilibrium and vector autoregression models in out-of-sample forecasting. We then demonstrate that the model can help make sense of extreme macroeconomic movements and apply the model to the three recent major economic crises of the euro area: the Financial crisis of 2007-2008 and the subsequent Great Recession, the European sovereign debt crisis, and the COVID-19 recession. We show that the model, due to non-linear responses, is capable of predicting a severe crisis arising endogenously around the most intense phase of the Great Recession in the euro area without any exogenous shocks. By analysing the COVID-19 recession, we further demonstrate the model for scenario analysis with exogenous shocks. Here we show that the model reproduces the observed deep recession followed by a swift recovery and also captures the persistent rise in inflation following the COVID-19 recession
Keywords: E70; E32; E37; 330; agent-based models; behavioural macro; macroeconomic forecasting; microdata; financial crisis; inflation and prices; coronavirus disease (COVID- 19).