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, ...

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 ...

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 ...

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 ...

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 ...