Deep Learning for Solving and Estimating Dynamic Macro-finance Models
Name
10614_2024_10693_ReferencePDF.pdf
Size
2.89 MB
Format
Adobe PDF
Checksum (MD5)
99737e5fe0948ee392f13b4683f73887
Author(s)
Fan, Benjamin
Qiao, Edward
Jiao, Anran
Gu, Zhouzhou
Li, Wenhao
Lu, Lu
Date Issued
August 9, 2024
Journal
Computational Economics
Publisher
Springer US
Citation
Fan, B., Qiao, E., Jiao, A. et al. Deep Learning for Solving and Estimating Dynamic Macro-finance Models. Comput Econ 65, 3885–3921 (2025).
Version
Author's final manuscript
Abstract
We develop a methodology that utilizes deep learning to simultaneously solve and estimate canonical continuous-time general equilibrium models in financial economics. We illustrate our method in two examples: (1) industrial dynamics of firms and (2) macroeconomic models with financial frictions. Through these applications, we illustrate the advantages of our method: generality, simultaneous solution and estimation, leveraging the state-of-art machine-learning techniques, and handling large state space. The method is versatile and can be applied to a vast variety of problems.
MIT Department
Massachusetts Institute of Technology. Department of Mathematics
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/s10614-024-10693-3
