This session aimed to gain insight into the macroeconomic models and inputs used at the decision-making level. In his talk, Robert Arnold, Congressional Budget Office (CBO), echoed Stock by stressing ...

This study seeks to construct a basic reinforcement learning-based AI-macroeconomic simulator. We use a deep RL (DRL) approach (DDPG) in an RBC macroeconomic model. We set up two learning scenarios, ...

Central banks and other policy institutions have a long history of using macroeconomic models to help prepare forecasts and to quantify the economic consequences of various policies. Likewise, private ...

Agent-based macroeconomic modelling represents a paradigm shift from traditional aggregate frameworks by simulating economies as networks of heterogeneous decision-making units whose interactions give ...

MANAGE-WB and MFMod have been refined to simulate the macroeconomic impacts of climate change, encompassing both transition (shift towards a low-carbon economy) and physical risks (direct climate ...

The macro economy, like the global climate, is a complex system (highly nonlinear and buffeted by random shocks) that defies attempts to model it and predict its future path. The challenge of ...

Workshop attendees participated in breakout groups broadly themed around the workshop’s sessions: macroeconomic modeling, economic and financial impacts of the energy transition, and public and ...