joe-data-analysis

name	joe-data-analysis
description	Use when designing the Monte Carlo study and empirical illustration that demonstrate a Journal of Econometrics (JoE) method works in finite samples. Covers size/power simulation design, DGP stress tests, and the role of the applied illustration relative to the theory.

Monte Carlo & Empirical Illustration (joe-data-analysis)

When to trigger

• The theorems are settled but the finite-sample evidence is thin or one-off
• A simulation reports point estimates but no size/power, or never stresses the assumptions
• You are unsure how large or how diverse the Monte Carlo design must be
• You have an empirical illustration but it is doing the wrong job (over- or under-claiming)

What "data analysis" means at a methodology journal

At the Journal of Econometrics the empirical work serves the method, not the other way around. A theorem describes behavior as $n\to\infty$; the Monte Carlo shows the asymptotics bite at realistic sample sizes, and the empirical illustration shows the method is usable and yields a sensible answer on real economic data. The applied illustration is a demonstration, not the paper's primary contribution — purely applied work without a methodological advance is out of scope here. Build both as evidence that the formal claims hold.

Monte Carlo design

Report the right quantities

• Estimators: bias, RMSE, coverage of confidence intervals.
• Tests: empirical size at nominal 5%/10%, then size-adjusted power curves. Over-rejection that vanishes only at huge $n$ is a finding, not a footnote.
• Compare against the nearest existing method on identical DGPs (ties back to joe-literature-positioning).

Stress the assumptions, do not flatter them

• Vary sample size (including small $n$ where asymptotics may fail).
• Vary the DGP: error distributions (heavy tails, heteroskedasticity), dependence (serial/cluster/spatial), degree of endogeneity or identification strength, dimension.
• Vary tuning parameters (bandwidth, lag length, penalty, number of moments) and show sensitivity.
• Include designs near the boundary of your conditions — that is where referees look.

Computational hygiene

• Fix and report seeds; report replication count and Monte Carlo standard errors so differences are not noise.
• Parallelize heavy designs; record runtime/hardware for the replication archive.

Finite-sample stress grid

Build the Monte Carlo grid around the theorem's weak points, not around flattering defaults:

Dimension	Minimum stress case
Sample size	A small or moderate $n$ where the asymptotic approximation is plausibly strained.
Identification strength	Weak instruments, near-collinearity, boundary parameters, local-to-zero effects, or sparse support as relevant.
Error process	Heavy tails, heteroskedasticity, serial/cross-sectional dependence, or clustering that matches the target application.
Tuning	Bandwidth, penalty, lag, moments, sieve dimension, or bootstrap choice varied enough to show stability.
Competitor	The closest existing estimator/test run on exactly the same DGP and reporting scale.

Pre-register the cells in the simulation plan, then mark any post-hoc additions as diagnostics. JoE referees punish Monte Carlos that prove only that the authors found a friendly DGP.

Empirical illustration

• Pick a dataset where the method's advantage is visible (the problem it solves actually occurs).
• Show the method changes a conclusion or sharpens inference relative to standard practice — that is the payoff.
• Keep claims proportionate: this is an illustration of the tool, not a causal study. Do not oversell the applied finding.
• Cite the data with the Elsevier [dataset] tag and prepare materials for the archive (see joe-replication-and-data-policy).

Execution bridge (StatsPAI / Stata MCP)

Run the battery, don't just enumerate it. Full map: execution-with-mcp. Journal of Econometrics is a methods venue — estimator validity + simulation evidence are the contribution; pair estimates with diagnostics and Monte-Carlo where relevant.

• Many outcomes / specifications: romano_wolf (step-down FWER) or benjamini_hochberg.
• OVB sensitivity: oster_delta / sensemakr.
• Inference: wild_cluster_bootstrap (few clusters), twoway_cluster / conley.
• Re-fit off one handle: audit_result(result_id) lists missing checks + the exact suggest_function for each.
• Exhibits: etable / did_summary_to_latex from the handle — no retyped numbers.

Decisive checks in the body, exhaustive battery in the appendix. JF execution walkthrough.

Anti-patterns

• A single DGP at one sample size "confirming" the theory
• Reporting raw power without empirical size (size-distorted power is meaningless)
• Hiding tuning-parameter sensitivity or boundary cases
• An empirical section that drifts into an applied paper the method only decorates
• Unreported seeds / replication counts, so results are not reproducible

Output format

【MC estimators】bias / RMSE / coverage reported? [Y/N]
【MC tests】size at 5%/10% + size-adjusted power? [Y/N]
【DGP stress】distributions / dependence / tuning / boundary? [list]
【Benchmark】compared to nearest method on same DGP? [Y/N]
【Reproducibility】seeds + reps + MCSE reported? [Y/N]
【Illustration】method changes/sharpens a real conclusion? [Y/N]
【Next step】joe-tables-figures