EGM$^n$: The Sequential Endogenous Grid Method
EGM$^n$ The Sequential Endogenous Grid Method”
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.
Details
Authors
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.
Actions
Live Interactive Notebook
This material includes a Jupyter Notebook version. You can directly launch and interact with the Notebook within your browser using MyBinder via the "Launch" button(s) below.
Material Source Code
Econ-ARK materials are open source and available to view and clone from GitHub.
View Paper
An electronic version of the research paper associated with this material is avaiable to view and download.
How to Execute this Notebook (with conda)
Install miniconda on your computer
- Open a Terminal (MacOS) or the Anaconda Prompt (Windows)
- At a command line, change the working directory to the one where you want to install
- On MacOS/unix, if you install in the
/tmpdirectory, the repo will disappear at reboot: cd /tmp
- On MacOS/unix, if you install in the
git clone https://github.com/alanlujan91/SequentialEGM --recursivecd SequentialEGMconda env create -f ./binder/environment.yml --prefix ./condaenv- This creates
./condaenvinside your clone of the repo, containing dependencies. conda run --prefix ./condaenv pip install jupyterlabconda run --prefix ./condaenv jupyter-lab
Metadata
| Key | Value |
|---|---|
| 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.
|
| authors | {"family-names"=>"Lujan", "given-names"=>"Alan"} |
| cff-version | 1.2.0 |
| github_repo_url | https://github.com/alanlujan91/SequentialEGM |
| identifiers-paper | {"type"=>"url", "value"=>"https://alanlujan91.github.io/SequentialEGM/egmn/"} |
| message | If you use this software, please cite it as below. |
| notebooks | code/EGMN/example_ConsPensionModel.ipynb |
| remark-name | SequentialEGM |
| remark-version | |
| tags | REMARK Notebook |
| date |