A Probabilistic Solution to High-Dimensional Continuous-Time Macro and Finance Models - Ji Huang (Center for Economic Studies and ifo Institute (CESifo), 2023)
This paper introduces the probabilistic formulation of continuous-time economic models: forward stochastic differential equations (SDE) govern the dynamics of backward-looking variables, and backward SDEs capture that of forward-looking variables. Deep learning streamlines the search for the probabilistic solution, which is less sensitive to the "curse of dimensionality." The paper proposes a straightforward algorithm and assesses its accuracy by considering a multiple-country model with an explicit solution under symmetric states. Combining with the finite volume method, the algorithm can obtain global dynamics of heterogeneous-agent models with aggregate shocks, in which agents consider the distribution of individual states as a state variable.
Keywords: C63; G21; E44; 330; backward stochastic differential equation; deep reinforcement learning; the curse of dimensionality; heterogeneous-agent continuous-time model; finite volume method