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Autor:innen: 
Erscheinungsjahr: 
2023
Schriftenreihe/Nr.: 
CESifo Working Paper No. 10600
Verlag: 
Center for Economic Studies and ifo Institute (CESifo), Munich
Zusammenfassung: 
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.
Schlagwörter: 
backward stochastic differential equation
deep reinforcement learning
the curse of dimensionality
heterogeneous-agent continuous-time model
finite volume method
JEL: 
C63
G21
E44
Dokumentart: 
Working Paper
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