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+---
+# CFF required fields
+cff-version: 1.2.0
+title: "Deep learning for solving dynamic economic models"
+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
+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"
+
+# REMARK fields
+remark-version: "v1.0.0"
+remark-name: "DeepLearningKrusselSmith"
+github_repo_url: https://github.com/marcmaliar/deep-learning-euler-method-krusell-smith/
+notebooks:
+  - code/python/Main_KS.ipynb
+---
+
+# Deep learning for solving dynamic economic models
+
+This notebook solves a version of Krusell and Smith's (1998) heterogenous-agent model with idiosyncrastic and aggregate shocks, incomplete markets and borrowing constraints. It uses a deep learning Euler-equation method introduced by Maliar, Maliar and Winant (2018) in the paper "Deep learning for solving dynamic economic models", Journal of Monetary Economics 122, pp 76-101. https://lmaliar.ws.gc.cuny.edu/files/2021/09/JME2021.pdf
+
+We show a version of the Euler equation method that minimizes the sum of squared residuals in the equilibrium conditions. See [https://deepecon.org](https://deepecon.org) for documentation, updates and the other versions of the deep learning method (Bellman equation and life-time reward).