A Reinforcement Learning Approach to Solving Incomplete Market Models with Aggregate Uncertainty

Jirnyi, A., and V. Lepetyuk
WP-AD 2011-21

Paraules clau:: reinforcement learning,incomplete market,aggregate uncertainty,Krusell-Smith,knn estimator

Resum: We develop a method of solving heterogeneous agent models in which individual decisions depend on the entire cross-sectional distribution of individual state variables, such as incomplete market models with liquidity constraints. Our method is based on the principle of reinforcement learning, and does not require parametric assumptions on either the agents’ information set, or on the functional form of the aggregate dynamics.