Deep Learning for Solving and Estimating Dynamic Macro-finance Models

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2024-08-09 DOI:10.1007/s10614-024-10693-3

Benjamin Fan, Edward Qiao, Anran Jiao, Zhouzhou Gu, Wenhao Li, Lu Lu

引用次数: 0

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.

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深度学习用于求解和估算动态宏观金融模型

我们开发了一种方法，利用深度学习同时求解和估计金融经济学中的典型连续时间一般均衡模型。我们用两个例子来说明我们的方法：(1) 企业的产业动态；(2) 具有金融摩擦的宏观经济模型。通过这些应用，我们说明了我们方法的优势：通用性、同步求解和估计、利用最先进的机器学习技术以及处理大的状态空间。该方法用途广泛，可应用于各种问题。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.