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deep-learning-euler-method-krusell-smith

https://github.com/marcmaliar/deep-learning-euler-method-krusell-smith

Science Score: 44.0%

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    CITATION.cff file
    Found CITATION.cff file
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    Found .zenodo.json file
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    Scientific vocabulary similarity
    Low similarity (4.7%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

Basic Info
  • Host: GitHub
  • Owner: marcmaliar
  • License: mit
  • Language: Jupyter Notebook
  • Default Branch: main
  • Size: 476 KB
Statistics
  • Stars: 5
  • Watchers: 1
  • Forks: 4
  • Open Issues: 0
  • Releases: 0
Created over 2 years ago · Last pushed about 2 years ago
Metadata Files
Readme License Citation

README.md

Deep-Learning code for solving Krusell-Smith heterogeneous-agent model

This code solves the Krusell-Smith heterogeneous-agent model using the Euler method. It was written by Marc Maliar and Serguei Maliar following the JME paper (link).

The code is presented in the style of an Econ-Ark REMARK (link). The actual code is located in code/python/Main_KS.ipynb.

Running the code

The code can be run either locally or through Binder on the web. To run it in Binder, click the link below

Click this -> Binder

To run it locally, you can

  • run reproduce_jupyter.sh, which opens the code in a Jupyter notebook.
  • (reproduce.sh is deprecated and does the same thing as reproduce_jupyter.sh)

Results figure is saved to the figures directory.

Owner

  • Name: Marc Maliar
  • Login: marcmaliar
  • Kind: user

University of Chicago CS and Math graduate.

Citation (CITATION.cff) 

# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: "Deep learning for solving dynamic economic models\0"
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Lilia
    family-names: Maliar
    affiliation: >-
      a The Graduate Center, City University of New York,
      CEPR, and Hoover Institution, Stanford University
  - given-names: Serguei
    family-names: Maliar
    affiliation: Santa Clara University
  - given-names: Pablo
    family-names: Winant
    affiliation: ESCP Business School and CREST/Ecole Polytechnique
identifiers:
  - type: doi
    value: 10.1016/j.jmoneco.2021.07.004
repository-code: >-
  https://github.com/marcmaliar/deep-learning-euler-method-krusell-smith/
abstract: >-
  We introduce a unified deep learning method that solves
  dynamic economic models by casting them into nonlinear
  regression equations. We derive such equations for three
  fundamental objects of economic dynamics – lifetime reward
  functions, Bellman equations and Euler equations. We
  estimate the decision functions on simulated data using a
  stochastic gradient descent method. We introduce an
  all-in-one integration operator that facilitates
  approximation of high-dimensional integrals. We use neural
  networks to perform model reduction and to handle
  multicollinearity. Our deep learning method is tractable
  in large-scale problems, e.g., Krusell and Smith (1998).
  We provide a TensorFlow code that accommodates a variety
  of applications.
keywords:
  - Artificial intelligence
  - Machine learning
  - Deep learning
  - Neural network
  - Stochastic gradient
  - Dynamic models
  - Model reduction
  - Dynamic programming
  - Bellman equation
  - Euler equation
  - Value function
references:
  - type: article
    authors:
      - family-names: "Krusell"
        given-names: "Per"
      - family-names: "Smith, Jr."
        given-names: "Anthony A."
    title: "Income and Wealth Heterogeneity in the Macroeconomy"
    doi: "10.1086/250034"
    date-released: 1998-10-01
    publisher:
        name: "Journal of Political Economy"

GitHub Events

Total
  • Watch event: 3
  • Fork event: 1
Last Year
  • Watch event: 3
  • Fork event: 1

Dependencies

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