jme-data-analysis
Use when building or stress-testing the empirical/quantitative analysis for a Journal of Monetary Economics (JME) manuscript — VAR/SVAR, local projections, DSGE estimation, moment matching, IRFs, and FEVDs — to monetary-economics and macroeconomics norms. Covers estimation choices, inference, and robustness.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jme-data-analysis --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.
# Data & Quantitative Analysis (jme-data-analysis) ## When to trigger - The estimation runs but referees will question the specification or inference - You must decide between a VAR, a proxy-SVAR, and local projections - A DSGE is estimated/calibrated and needs convergence and fit diagnostics - The robustness battery for a macro paper is unclear ## Macro-empirical norms at JME JME analysis is **aggregate and policy-relevant**, so the workhorses are different from micro-econometrics. The core toolkit: - **VAR / SVAR / proxy-SVAR** for dynamic responses to identified shocks; report **impulse responses with confidence/credible bands**, lag selection, stability, and **forecast-error variance decompositions (FEVDs)**. - **Local projections (Jordà)** as a robustness counterpart to VAR IRFs; show both when feasible, since LP trades variance for robustness to misspecification. - **DSGE / quantitative models** estimated by Bayesian methods (Dynare) or calibrated to micro moments; report **prior/posterior plots, MCMC convergence, identification (Iskrev), and posterior predictive / second-moment fit**. - **Real-time data** (FRED/ALFRED vintages, Greenbook/Tealbook) where the information set m
- When to trigger
- Macro-empirical norms at JME
- Robustness battery (macro)
- Execution bridge (StatsPAI / Stata MCP)
- Checklist
- Anti-patterns
- Evidence pass for Journal of Monetary Economics
- Output format
What does the jme-data-analysis skill do?
Use when building or stress-testing the empirical/quantitative analysis for a Journal of Monetary Economics (JME) manuscript — VAR/SVAR, local projections, DSGE estimation, moment matching, IRFs, and FEVDs — to monetary-economics and macroeconomics norms. Covers estimation choices, inference, and robustness.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jme-data-analysis --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.