Mksc Tables Figures

Tables & Figures (mksc-tables-figures)

When to trigger

• Estimate or counterfactual tables are cluttered or not self-explanatory
• Comparative statics / elasticities are buried in text instead of an exhibit
• A figure does not make the model's mechanism or policy result legible
• Exhibits are off INFORMS house style

The exhibits a modeling paper needs

A Marketing Science paper is read through its model and its counterfactuals, so the core exhibits differ from an experiments paper:

• Estimates table. Structural parameters (preferences, dynamics, supply) with standard errors; group by demand vs. supply; label normalizations. Report elasticities (own/cross-price) and implied margins, not just raw coefficients.
• Model-fit table/figure. Predicted vs. actual moments/shares/prices; in-sample and holdout. Make goodness-of-fit visible.
• Comparative-statics figure (analytical papers). Plot how equilibrium prices/profit/advertising move with the key parameter; annotate the counterintuitive region that is the contribution.
• Counterfactual/policy table. Baseline vs. counterfactual outcomes (prices, shares, profit, consumer surplus/welfare) with uncertainty intervals; decompose the driving mechanism.
• Identification/sensitivity exhibit where helpful (sensitivity of estimates to moments; first-stage strength).

INFORMS house style

• Number tables and figures consecutively; give each a self-contained caption that states the model/sample and what the reader should conclude.
• Define every symbol and notation used; report units and the estimand.
• Note the estimator and standard-error type in the table notes (e.g., "GMM; standard errors in parentheses").
• Keep figures clean and grayscale-legible; the model's intuition should survive black-and-white printing.
• Heavy supporting exhibits (full parameter sets, additional counterfactuals, derivations) belong in the online appendix to keep the main text succinct.

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. Marketing Science is heavily structural/analytical; the chain below serves its reduced-form / field-experiment lane — structural demand and analytical modeling are outside this causal-inference toolchain.

• Tables: etable (multi-model columns) or did_summary_to_latex straight from the result_id.
• 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 and states the effect size in interpretable units.

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

Checklist

• Estimates table reports elasticities/margins, not just coefficients; SEs + estimator noted
• Model-fit exhibit (in-sample + holdout) included
• Comparative-statics figure for analytical results, annotated
• Counterfactual table: baseline vs. policy, with uncertainty + mechanism
• Every symbol/notation defined; captions self-contained; units stated
• Secondary exhibits moved to the online appendix

Anti-patterns

• A coefficient dump with no elasticities, margins, or interpretation.
• Counterfactual numbers with no uncertainty or baseline comparison.
• Figures relying on color that vanish in grayscale.
• Captions that name the table but not the conclusion.

Exhibit pass for Marketing Science

Treat this skill as an executable review pass, not a prose hint. First lock the demand/supply mechanism, fit evidence, and counterfactual decision margin; then judge whether the current manuscript answers the venue's real reader: quantitative marketing reviewers who read the model through the managerial counterfactual it makes possible.

• Do the pass: For every table or figure, state the estimand or object, sample or case base, uncertainty display, and one sentence the exhibit proves for the venue audience.
• Return a ledger: give claim / evidence / risk / manuscript location rows, so the next agent can edit rather than rediscover the issue.
• Sibling guard: compare against Journal of Marketing Research for empirical marketing breadth, Management Science for wider OR/MS reach, Quantitative Marketing and Economics for specialist modeling; if a sibling owns the contribution, recommend re-routing before polishing format.
• Stop condition: do not give submission-ready advice until the pack's resources/official-source-map.md has been checked for volatile rules and the manuscript has one concrete fix for the largest venue-specific risk.

Output format

【Estimates】params + elasticities/margins; SEs + estimator in notes
【Fit】predicted vs actual (in-sample/holdout) exhibit present?
【Comparative statics】figure for analytical claim, annotated?
【Counterfactual】baseline vs policy + uncertainty + mechanism
【Style】notation defined; captions self-contained; grayscale-safe
【Appendix】secondary exhibits relocated
【Next step】mksc-writing-style