Why
Strong toolkits, siloed communities
Computational economics has more than a dozen serious open-source toolkits across MATLAB, Python and Julia, each with distinct strengths — and no easy way to compare them, no shared library of canonical models, no on-ramp to the ecosystem as a whole. Users stay with the first tool they learn, and the projects themselves have no standing channel connecting them.
Community first, corpus second
The first thing this project builds is the community: the toolkit projects united in one group, with common pathways of communication and a shared library of tooling maintained together. The corpus of baseline models is the goal, but it is the community that produces it and keeps it alive.
- A community resource, built together
- The field's first cross-toolkit reference — an on-ramp for new users and a showcase for the projects behind the tools.
- A test corpus for the semantics track
- When toolkit teams state formally what their model files mean, a curated set of worked models is what those statements have to account for.
- Substrate for AI assistance
- Exactly the corpus an assistant needs to answer which tool fits my problem? and translate my model from X to Y.
- A proving ground for standards
- Shared schemas — steady states, IRFs, Jacobians — demonstrated in working code rather than prose.
The library
The working group publishes a set of baseline models. Each project chooses as many as its toolkit can solve — several or just one — and submits a tutorial for each. Four models seed the set.
Aiyagari · Krusell–Smith
Incomplete-markets heterogeneous agents
A two-asset HANK model
Heterogeneous-agent New Keynesian
A small New Keynesian DSGE model
Representative-agent DSGE
A baseline agent-based macro model
Macro from interacting heterogeneous agents
Open slots fill as projects join — one per participating toolkit. A model earns its place in the set by being solved; each row's template pins what has to line up.
How it works
Each participating project makes two contributions. The first makes the toolkits comparable; the second lets each project show what it is actually best at.
Baselines from the shared set
Each project solves the baseline models it has chosen — same calibration, same reported objects — so results line up side by side and divergences are visible rather than buried.
An author's-choice showcase
A tutorial of the project's own choosing, on the problems its toolkit was built for. A common baseline alone would flatten the differences that matter; this is where each project argues its own case.
Both are published in the toolkit's own notation — no house dialect — with per-model metadata, honest notes on tradeoffs, and CI that runs each implementation and flags breakage.
Baseline templates
Each model in the set gets a baseline template: a precise statement of what the model is — enough that results line up across toolkits — without prescribing how it should be solved.
The set is published
Each model is anchored on a published paper with its own calibration.
Projects choose models
One is enough, several are welcome; the choice is per model, with no obligation beyond it.
A template notebook each
Published as a PDF and a notebook, model statement and explanatory text already written.
Implementations line up
Each project completes the notebook in its own toolkit — and that notebook is its tutorial.
- Functional forms, timing conventions, normalizations
- Parameter values, and where they come from
- Target moments of the exogenous processes
- Which objects are reported, in what units
- The tolerance at which two results count as agreeing
- Solution method and algorithm
- Grids, discretization, basis functions
- How the exogenous process is discretized
- Language, code structure, API
- Everything the toolkit exists to do differently
Divergence beyond tolerance is documented and attributed to method — a result of the exercise, not a failure of it. A project that cannot match one element of a template contributes with a documented deviation rather than dropping out. Phase 1 publishes no timings and no ranking of toolkits.
The ask
What each project provides
- Agreement in principle to take part.
- By end of 2026, at least one baseline model solved against its template, so results line up side by side.
- An author's-choice showcase tutorial highlighting where the toolkit is strongest.
- Both contributions authored and maintained by the project — CC-BY for text, an OSI-approved licence for code.
What each project keeps
- Authorship credit for its contributions.
- Editorial control over how its tool is presented.
- The right to update or withdraw contributions at any time.
QuantEcon curates and publishes; it does not own anyone's work or rank the toolkits.
Who does what
QuantEcon
Publishes and maintains the library, sets editorial standards for what goes in, and builds the shared tooling: local-run handlers for MATLAB, Octave and Julia, schema validators, converters between output formats.
Participating projects
Author and maintain their own implementations and tutorials — toolkit-specific content stays theirs, and merges to it happen at their discretion.
SCE Working Group 1
Builds and releases the baseline set and templates. The library runs independently of the group; QuantEcon attends as a member to see where it can assist the group's semantics work.
Project A
The Semantics of Economic Models — the working group's companion track, in which toolkit maintainers write a solver-independent account of what their model files mean, published as a journal special issue. The library's worked models are the corpus those accounts have to account for.
Roadmap
Phase 1 · Community library
6–12 mo- 3–4 canonical models in 2–3 toolkits each, published on a static JupyterBook site.
- Open-source repository, PRs welcome.
- Per-project tutorials and per-model metadata.
- CI runs the implementations and flags breakage.
- An AI advisor MVP built on the corpus.
Phase 2 · Reference standard
12–24 mo- Shared output schemas and a queryable index.
- Cross-toolkit validation reports: where do the same model's outputs diverge?
- Library models expressed in a shared semantic representation, building on Econ-ARK's DP schema.
- Documentation of that design, plus a how-to tutorial.
Contribute
The library is an open-source repository and takes pull requests from anyone, member or not; changes to a project's implementation merge at that project's discretion.
QuantEcon/community-library Hosted and published by QuantEcon · one model per directory: implementation, tutorial text and metadata together clone ↗