jape-data-analysis
Use when running estimation and inference for a Journal of Applied Econometrics (JAE) manuscript so the analysis is reproducible and archive-ready — every table/figure regeneratable from plain-text data and programs you will deposit in the JAE Data Archive. Covers robust inference, master-script discipline, Monte Carlo evidence, and the archive's plain-ASCII/CSV format rule.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jape-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 Analysis for JAE (jape-data-analysis) ## When to trigger - Setting up the estimation pipeline for a JAE paper - Choosing inference (HAC, clustered, bootstrap) for real-data estimates - Making sure the analysis will satisfy the JAE Data Archive before you submit ## Analyze for reproducibility from day one JAE's identity is **replicable** applied work, and accepted papers must deposit data and (typically) programs in the **JAE Data Archive**. Structure the analysis as if a referee will rerun it: - One **master script** (`run_all.do` / `make` / `Snakefile`) regenerates **every** exhibit from raw inputs — no manual steps. - Pin software (`version` in Stata, `renv`/`sessionInfo()` in R, pinned `requirements.txt` in Python); fix and log all seeds. - Document sample construction, variable definitions, and estimation commands so the path from raw or documented restricted data to final exhibits is transparent. - Write intermediate and final results to **plain text** (CSV/TXT), matching the archive format — never let `.dta` be the only copy. ## Inference appropriate to real data Match inference to structure: HAC/Newey–West for serial correlation; cluster-robust SEs for panels; wild/cl
- When to trigger
- Analyze for reproducibility from day one
- Inference appropriate to real data
- Monte Carlo, where used
- Claim-to-archive ledger
- Inference picker for archive-bound estimates
- Few-cluster and weak-instrument trip wires
- Worked numbers: a 13-state policy panel (illustrative)
- Monte Carlo specification block
- Execution bridge (StatsPAI / Stata MCP)
- Output format
- Supplementary resources
What does the jape-data-analysis skill do?
Use when running estimation and inference for a Journal of Applied Econometrics (JAE) manuscript so the analysis is reproducible and archive-ready — every table/figure regeneratable from plain-text data and programs you will deposit in the JAE Data Archive. Covers robust inference, master-script discipline, Monte Carlo evidence, and the archive's plain-ASCII/CSV format rule.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jape-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.