A Probabilistic Solution to High-Dimensional Continuous-Time Macro and Finance Models

Arbeitspapier

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.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: CESifo Working Paper ; No. 10600

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Computational Techniques; Simulation Modeling ; Banks; Depository Institutions; Micro Finance Institutions; Mortgages ; Financial Markets and the Macroeconomy
• Thema: backward stochastic differential equation ; deep reinforcement learning ; the curse of dimensionality ; heterogeneous-agent continuous-time model ; finite volume method

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Huang, Ji
• Ereignis: Veröffentlichung
• (wer): Center for Economic Studies and ifo Institute (CESifo)
• (wo): Munich
• (wann): 2023

Weitere Informationen

• Handle: http://hdl.handle.net/10419/279351
• Letzte Aktualisierung: 10.03.2025, 11:46 MEZ