aejmac-theory-model
Use when the quantitative model is the bottleneck for an American Economic Journal: Macroeconomics (AEJ: Macro) manuscript — DSGE, New Keynesian, heterogeneous-agent (HANK / Aiyagari-Bewley), or structural estimation — and calibration, parameter identification, solution accuracy, or counterfactual validity need discipline. For empirical shock identification see aejmac-identification.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill aejmac-theory-model --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Quantitative Theory & Model Discipline (aejmac-theory-model) ## When to trigger - Parameters are calibrated or estimated but it is unclear *what disciplines* each one - A DSGE/HANK model is solved but the solution method / accuracy is unstated - A counterfactual or welfare number is reported with no validity argument (Lucas critique) - Untargeted moments are never shown, so the model's fit is as
What does the aejmac-theory-model skill do?
Use when the quantitative model is the bottleneck for an American Economic Journal: Macroeconomics (AEJ: Macro) manuscript — DSGE, New Keynesian, heterogeneous-agent (HANK / Aiyagari-Bewley), or structural estimation — and calibration, parameter identification, solution accuracy, or counterfactual validity need discipline. For empirical shock identification see aejmac-identification.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill aejmac-theory-model --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Awesome-Journal-Skills, a repository with 909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.