Jbes Data Analysis

Monte Carlo & Empirical Application (jbes-data-analysis)

When to trigger

• The asymptotic theory exists but the simulation study is thin or one-sided
• The empirical application is a toy illustration rather than a substantive use
• Reviewers will ask "does the method actually work in finite samples / on real data?"
• You need to choose DGPs, baselines, and an application that show the method's value

Why this matters at JBES

JBES is a methods-with-empirics journal: a contribution is incomplete without finite-sample evidence and a substantive empirical application in microeconomics, macroeconomics, business, or finance. The simulation study is how you demonstrate the asymptotics bite at realistic sample sizes; the application is how you demonstrate clear empirical relevance. Both are evaluated by method experts who will reproduce or interrogate them.

Monte Carlo design

• DGPs that span the conditions: include cases that satisfy your assumptions and cases that stress or violate them (dependence, heavy tails, weak identification, high dimension) so the breakdown frontier is visible.
• Sample-size grid: show size/power/coverage/bias/RMSE converging as n grows.
• Honest baselines: compare against the relevant incumbent method(s) under identical DGPs.
• Reported MC uncertainty: give Monte Carlo standard errors of rejection rates; fix and report seeds; document the number of replications and runtime.

The empirical application

• Use a real, recognizable data set (e.g., FRED-MD macro series, CRSP/Compustat finance, IPUMS micro) that the method's novelty genuinely helps.
• Show the new method changes a substantive conclusion or enables an analysis prior methods could not.
• Report inference appropriate to the data (HAC/cluster/dependence-robust); include diagnostics.

Execution bridge (StatsPAI / Stata MCP)

Run the battery, don't just enumerate it. Full map: execution-with-mcp. JBES is a business / economic-statistics venue — reviewers weigh estimator validity and simulation evidence, so pair every estimate with its diagnostics and, where relevant, a Monte-Carlo check.

• Many outcomes / specifications: romano_wolf (step-down FWER, accounts for cross-test correlation) or benjamini_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 exact suggest_function for each — no guessing the battery.
• Exhibits: etable / did_summary_to_latex from 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.

Checklist

• DGPs include both favorable and assumption-stressing regimes
• Sample-size grid displays the asymptotics taking hold
• Incumbent baselines simulated under identical DGPs
• Monte Carlo standard errors, seeds, and replication counts reported
• A substantive real-data application, not a toy illustration
• The application uses the method's novelty and changes/enables a conclusion
• All numbers regenerate from code (see jbes-replication-and-data-policy)

Anti-patterns

• Simulating only DGPs that flatter the method; hiding breakdown
• Reporting rejection rates with no Monte Carlo standard errors
• A toy application with no substantive empirical payoff (off-scope at JBES)
• Omitting the incumbent baseline, so "improvement" is unquantified
• Cherry-picked sample sizes that mask poor small-n behavior

Referee-pushback patterns on the evidence (venue-specific fixes)

Worked vignette: validating a new long-horizon forecast test

A hypothetical JBES paper proposes a HAC-robust test of equal long-horizon predictability, validated on FRED-MD inflation forecasts (numbers illustrative). The Monte Carlo calibrates the DGP to FRED-MD persistence (AR root near 0.97) and overlapping-horizon dependence, not iid noise; at n=240 the test holds an illustrative size of 5.4% versus nominal 5%, while the Diebold-Mariano benchmark over-rejects at 9.1% under the same DGP. The application then reverses a borderline DM verdict on whether a factor-augmented model beats the random walk at 12 months — a substantive payoff, not a toy. Calibration anchor (hedged): JBES weights careful simulation and a real application roughly equally; a paper strong on only one axis is exposed.

Evidence pass for Journal of Business & Economic Statistics

Run this as a concrete capability pass. First lock the statistical estimand, identification/simulation evidence, empirical illustration, and reproducibility path; then test whether the manuscript addresses econometrics/statistics reviewers who expect methodological credibility plus a business or economic use case.

• Primary move: Audit unit, comparison, uncertainty, missingness, sensitivity, and reproducibility before making any prose or submission recommendation.
• Decision ledger: return claim / evidence / blocker / next edit rows so the next pass can patch the manuscript directly.
• Sibling comparison: compare against Journal of Econometrics for theory-heavy methods, Econometric Theory for proof-first work, Quantitative Economics for economics-theory methods; if the neighboring outlet has the stronger audience claim, recommend re-routing before polishing.
• Verification floor: before submission-ready advice, re-open resources/official-source-map.md for volatile rules and name the one unresolved fact that could change the recommendation.

Output format

【DGPs】favorable + stress regimes covered? [Y/N]
【n grid】asymptotics visible as n grows? [Y/N]
【Baselines】incumbent(s) under identical DGPs? [Y/N]
【MC uncertainty】MC SEs + seeds + reps reported? [Y/N]
【Application】substantive, uses the novelty? [Y/N]
【Next step】jbes-tables-figures

Source: brycewang-stanford/Awesome-Journal-Skills → Journal-of-Business-and-Economic-Statistics-Skills/skills/jbes-data-analysis/SKILL.md