sequentialegm
Science Score: 44.0%
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✓codemeta.json file
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○Scientific vocabulary similarity
Low similarity (13.1%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: alanlujan91
- License: mit
- Language: Jupyter Notebook
- Default Branch: main
- Homepage: https://alanlujan91.github.io/SequentialEGM/
- Size: 1.22 GB
Statistics
- Stars: 6
- Watchers: 2
- Forks: 5
- Open Issues: 4
- Releases: 1
Metadata Files
README.md
title: "EGMⁿ: The Sequential Endogenous Grid Method" author: "Alan Lujan"
date: 2023/02/15
EGMⁿ: The Sequential Endogenous Grid Method
Abstract
Heterogeneous agent models with multiple decisions are often solved using inefficient grid search methods that require a large number of points and are time intensive. This paper provides a novel method for solving such models using an extension of the endogenous grid method (EGM) that uses Gaussian Process Regression (GPR) to interpolate functions on unstructured grids. First, separating models into smaller, sequential problems allows the problems to be more tractable and easily analyzed. Second, using an exogenous grid of post-decision states and solving for an endogenous grid of pre-decision states that obey a first order condition greatly speeds up the solution process. Third, since the resulting endogenous grid can often be curvilinear at best and unstructured at worst, GPR provides an efficient and accurate method for interpolating the value, marginal value, and policy functions. Applied sequentially to each decision within the overarching problem, the method is able to solve heterogeneous agent models with multiple decisions in a fraction of the time and with less computational resources than are required by standard grid search methods currently used. This paper also illustrates how this method can be applied to a number of increasingly complex models. Software is provided in the form of a Python module under the HARK package.
Owner
- Name: Alan Lujan
- Login: alanlujan91
- Kind: user
- Location: Rockville, MD
- Company: The Ohio State University
- Repositories: 7
- Profile: https://github.com/alanlujan91
PhD candidate at The Ohio State University working on heterogeneous agent models and quantitative macroeconomics.
Citation (CITATION.cff)
cff-version: "1.2.0"
message: "If you use this software, please cite it as below."
authors:
- family-names: "Lujan"
given-names: "Alan"
title: "EGMⁿ: The Sequential Endogenous Grid Method"
abstract: |
Heterogeneous agent models with multiple decisions are often solved using inefficient grid search methods that require many evaluations and are slow. This paper provides a novel method for solving such models using an extension of the Endogenous Grid Method (EGM) that uses Gaussian Process Regression (GPR) to interpolate functions on unstructured grids. First, I propose an intuitive and strategic procedure for decomposing a problem into subproblems which allows the use of efficient solution methods. Second, using an exogenous grid of post-decision states and solving for an endogenous grid of pre-decision states that obey a first-order condition greatly speeds up the solution process. Third, since the resulting endogenous grid can often be non-rectangular at best and unstructured at worst, GPR provides an efficient and accurate method for interpolating the value, marginal value, and decision functions. Applied sequentially to each decision within the problem, the method is able to solve heterogeneous agent models with multiple decisions in a fraction of the time and with less computational resources than are required by standard methods currently used. Software to reproduce these methods is available under the https://econ-ark.org/ project for the python programming language.
GitHub Events
Total
- Delete event: 2
- Issue comment event: 2
- Push event: 105
- Pull request event: 6
- Create event: 4
Last Year
- Delete event: 2
- Issue comment event: 2
- Push event: 105
- Pull request event: 6
- Create event: 4
Issues and Pull Requests
Last synced: 10 months ago
All Time
- Total issues: 0
- Total pull requests: 3
- Average time to close issues: N/A
- Average time to close pull requests: 4 months
- Total issue authors: 0
- Total pull request authors: 1
- Average comments per issue: 0
- Average comments per pull request: 0.67
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 3
Past Year
- Issues: 0
- Pull requests: 2
- Average time to close issues: N/A
- Average time to close pull requests: about 1 month
- Issue authors: 0
- Pull request authors: 1
- Average comments per issue: 0
- Average comments per pull request: 0.5
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 2
Top Authors
Issue Authors
Pull Request Authors
- dependabot[bot] (14)
- alanlujan91 (3)
- camriddell (1)
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Dependencies
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- actions/checkout v4 composite
- actions/setup-python v5 composite
- codecov/codecov-action v4.0.1 composite
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- ConSav *
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