Jbes Tables Figures

Tables & Figures (jbes-tables-figures)

When to trigger

• Simulation tables are dense and a reader cannot read off whether the method controls size
• Size, power, coverage, bias, and RMSE are scattered instead of side-by-side
• Figures lack confidence bands, or compare against no baseline
• Exhibit notes do not state the DGP, sample size, replications, or nominal level

What JBES exhibits must communicate

A JBES paper is judged by method experts who read the tables as the evidence. Exhibits carry two distinct jobs: the Monte Carlo exhibits that establish the method's statistical properties, and the empirical exhibits that establish relevance on real data. Both must be readable by a statistician and an applied economist, since the journal bridges the two communities.

Monte Carlo exhibits

• Put size and power side-by-side across DGPs and sample sizes; mark the nominal level so over/under-rejection is obvious.
• For interval methods, report coverage and average length together; for point estimators, bias and RMSE together.
• Always show the incumbent baseline in the same table under identical DGPs.
• Figures: size-power curves, coverage-vs-n plots, empirical-vs-nominal QQ plots, sampling distributions with the asymptotic reference overlaid.

Empirical exhibits

• Report estimates with appropriate (HAC/cluster/dependence-robust) standard errors.
• Show the substantive payoff: what the new method changes relative to the standard approach.

Self-contained notes (every exhibit)

State the DGP or data source, sample size(s), number of Monte Carlo replications, nominal level, the estimator/test, the inference method, and units. A reader should not need the body to interpret the table.

Execution bridge (StatsPAI / Stata MCP)

Generate exhibits from the fitted result, not by retyping numbers (the usual source of body-vs-appendix drift). Full map: execution-with-mcp.

• Tables: etable (multi-model columns) or did_summary_to_latex straight from the result_id — one variable definition, one set of numbers, body and appendix in sync.
• Figures: plot_from_result / enhanced_event_study_plot / event_study_table — axis units and the SE/clustering note baked in.
• Every note names the estimator + clustering (from the result's diagnostics) and states the magnitude in interpretable units.

See a full fitted-result → exhibit chain in the JF execution walkthrough.

Checklist

• Size and power readable side-by-side, with nominal level marked
• Coverage shown with average length; bias shown with RMSE
• Incumbent baseline in the same exhibit under identical DGPs
• Figures have confidence bands / reference distributions
• Every note states DGP/data, n, replications, level, method, units
• Vector output (PDF/EPS); no chartjunk; legible in print
• Numbers match the manuscript text and the code output

Anti-patterns

• A wall of numbers where size control cannot be read off at a glance
• Reporting power without size (or coverage without length)
• Omitting the baseline, so "improvement" is unquantified in the exhibit
• Notes that force the reader back into the body to decode the table
• Figures with no uncertainty and no reference distribution

Worked vignette: a size-and-power table a referee can read

A hypothetical JBES paper reports a Monte Carlo size-power table for a new break test (numbers illustrative). The good version puts size and power side-by-side across n ∈ {120, 240, 480}, marks the 5% level in the caption, and shows the CUSUM benchmark in the same rows — so a reader sees the new test holds size at 5.2% while CUSUM over-rejects at 8.7%. The note states the DGP, replications, MC standard errors, level, and method.

Exhibit-pushback patterns (venue-specific fixes)

Calibration anchor (hedged): JBES exhibits serve two audiences — a statistician reading Monte Carlo properties and an applied economist reading the payoff — so every table must be legible to both. Exact format specifics are live T&F preflight items.

Exhibit 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: For every table or figure, state the object, sample/case base, uncertainty display, and one sentence the exhibit proves for this venue.
• 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

【Exhibit】Monte Carlo / empirical
【Size+power】side-by-side with level marked? [Y/N]
【Coverage/length or bias/RMSE】paired? [Y/N]
【Baseline】incumbent in same exhibit? [Y/N]
【Notes】DGP/data, n, reps, level, method, units present? [Y/N]
【Next step】jbes-writing-style