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

Ji Huang

No 10600, CESifo Working Paper Series from CESifo

Abstract: 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: backward stochastic differential equation; deep reinforcement learning; the curse of dimensionality; heterogeneous-agent continuous-time model; finite volume method (search for similar items in EconPapers)
JEL-codes: C63 E44 G21 (search for similar items in EconPapers)
Date: 2023
New Economics Papers: this item is included in nep-cmp
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.cesifo.org/DocDL/cesifo1_wp10600.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ces:ceswps:_10600

Access Statistics for this paper

More papers in CESifo Working Paper Series from CESifo Contact information at EDIRC.
Bibliographic data for series maintained by Klaus Wohlrabe ().