thedixitjain 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 (
versionin Stata,renv/sessionInfo()in R, pinnedrequirements.txtin 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
.dtabe the only copy.
Inference appropriate to real data
Match inference to structure: HAC/Newey–West for serial correlation; cluster-robust SEs for panels; wild/cluster bootstrap with few clusters; weak-IV-robust inference for IV. State which adjustment you use and why.
Monte Carlo, where used
If you stress-test a method, report the DGP, sample sizes, replication count, and seeds, and ship the simulation script so the tables regenerate exactly. Simulations should illuminate the empirical problem, not stand alone.
Claim-to-archive ledger
Before submission, create a ledger with one row per main claim:
Claim | Exhibit | Input data | Code target | Inference choice | Archive file
Use it to catch unsupported claims and archive gaps. If a row has no code target, the exhibit is not reproducible. If a row has no inference rationale, a referee can challenge the standard errors before engaging the economics. If a row has no archive file, the Data Archive package will not reproduce the published paper.
The ledger also clarifies what belongs in the online appendix: diagnostics and robustness checks that support a ledger row should be preserved there, even if the main article cannot spare space.
Inference picker for archive-bound estimates
JAE referees rerun your code, so the inference choice must be defensible and visible in the deposited scripts. Default mapping:
| Data structure | Expected inference at JAE | What the table note states |
|---|---|---|
| Time series, serial correlation | HAC / Newey–West; justify bandwidth/lag choice | Kernel, lag truncation, sample span |
| Panel, many clusters (≈40+) | Cluster-robust at the treatment/assignment level | Cluster variable and count |
| Panel, few clusters (<~30) | Wild cluster bootstrap (Rademacher/Webb weights) | Bootstrap type, replications, seed |
| IV with modest first stage | Weak-IV-robust (Anderson–Rubin CI; effective F) | First-stage F alongside the 2SLS column |
| Forecast comparisons | Diebold–Mariano-style tests with HAC variance | Loss function and comparison window |
Few-cluster and weak-instrument trip wires
Two inference failures recur in JAE referee reports: cluster-robust SEs treated as valid with a handful of clusters, and 2SLS t-statistics reported without weak-IV diagnostics. Pre-empt both — report the cluster count in every clustered table, switch to wild cluster bootstrap p-values when it is small, and pair any IV column with the effective first-stage F plus an Anderson–Rubin interval. Put the bootstrap loop in the deposited code with its seed so the archived p-value regenerates digit-for-digit.
Worked numbers: a 13-state policy panel (illustrative)
A staggered state-policy evaluation with 13 treated-side clusters: CRVE gives β = −0.042, s.e. 0.018, p ≈ 0.02; the wild cluster bootstrap (Webb weights, 9,999 draws, seed logged) gives p ≈ 0.08. The JAE-grade move is to report both, lead with the bootstrap, and let the online appendix carry the full grid (weight choice, replications, leave-one-cluster-out). The archived infer_main.do regenerates each p-value; the readme names the seed. Hiding the fragile p-value is the move referees at this venue are trained to catch.
Monte Carlo specification block
When simulation evidence backs the empirical design, register it like an exhibit:
DGP: [equations + parameter values, calibrated to the application]
Sample sizes: [matching the real data, plus stress values]
Replications: [count] | Seed: [value] | Software: [version]
Script: sims/mc_main.R → outputs tables/mc_table3.csv
Question answered: [which empirical inference concern this resolves]
Execution bridge (StatsPAI / Stata MCP)
Run the battery, don't just enumerate it. Full map:
execution-with-mcp. Applied econometrics: the estimator and its diagnostics are themselves the contribution, so foreground the weak-IV / pre-trend / sensitivity tooling.
- Many outcomes / specifications:
romano_wolf(step-down FWER, accounts for cross-test correlation) orbenjamini_hochberg— report the adjusted threshold. - OVB sensitivity:
oster_delta/sensemakr— the confounder strength that would overturn the headline. - Inference:
wild_cluster_bootstrap(few clusters),twoway_cluster/conley. - Re-fit off one handle:
audit_result(result_id)lists the missing checks and the exactsuggest_functionfor each — no guessing the battery. - Exhibits:
etable/did_summary_to_latexfrom the handle — no retyped numbers.
Keep the decisive checks in the body and the exhaustive (now actually-run) battery in the appendix. See the executed chain in the JF execution walkthrough.
Output format
【Master script】regenerates all exhibits? [Y/N]
【Repro】versions pinned + seeds fixed? [Y/N]
【Inference】HAC / clustered / bootstrap / weak-IV — matched? [Y/N]
【Few-cluster guard】cluster counts reported; bootstrap where small? [Y/N]
【Archive format】plain CSV + readme alongside (not .dta-only)? [Y/N]
Supplementary resources
../../resources/external_tools.md— estimation, inference, reproducibility tooling../../resources/official-source-map.md— archive format-rule sources
Source: brycewang-stanford/Awesome-Journal-Skills → Journal-of-Applied-Econometrics-Skills/skills/jape-data-analysis/SKILL.md