| name | mksc-data-analysis |
| description | Use when estimating and validating the model for a Marketing Science manuscript — running structural estimation (GMM/MLE/SMM/Bayes), checking identification empirically, assessing model fit, computing counterfactuals, and preparing the replication package. Executes the analysis; it does not design the model (mksc-theory-development) or choose the genre (mksc-methods). |
Estimation, Fit & Counterfactuals (mksc-data-analysis)
When to trigger
- The model is specified and it is time to estimate and report
- Estimates exist but identification, fit, or counterfactuals are not yet convincing
- A reviewer says "the parameters are not credibly identified" or "the counterfactual is not validated"
- You need the replication package (data + estimation code) ready for acceptance
Estimate, then prove identification empirically
- Run the estimator matched to the model: GMM with the stated moment conditions (BLP), MLE/SMLE, simulated method of moments, or MCMC for hierarchical Bayes. Report standard errors that respect the estimation (e.g., GMM/sandwich, bootstrap, or posterior intervals) and the optimizer/convergence diagnostics.
- Demonstrate identification, not just assert it: show the identifying variation moves the relevant moments; report sensitivity of estimates to instruments; where feasible, a Monte Carlo recovering known parameters or a sensitivity-of-estimates-to-moments analysis strengthens the claim.
- First-stage/instrument strength for IV/GMM; relevance and exclusion discussed.
Assess model fit before trusting counterfactuals
- Report in-sample fit (predicted vs. actual shares/prices/moments) and, where possible, out-of-sample or holdout validation.
- Check economic plausibility of estimates (own-/cross-price elasticities, margins implied by supply FOCs, discount factors) against priors and institutional facts.
- For Bayesian models, report convergence (R-hat, effective sample size) and posterior predictive checks.
Counterfactuals are the payoff
- Re-solve the model under the policy/counterfactual, holding fixed only what theory says is fixed; recompute equilibrium prices/quantities where firms re-optimize.
- Report magnitudes with uncertainty (delta-method or simulation-based intervals), and decompose the mechanism driving the result.
- Discuss the scope and assumptions under which the counterfactual is valid.
Robustness
- Alternative specifications (functional form, instruments, heterogeneity), subsamples, and alternative normalizations.
- Show the headline result and key counterfactual survive the changes a referee will request.
Replication package (plan now, deposit on acceptance)
Per the Marketing Science Replication and Disclosure Policy, accepted papers submit data and estimation code sufficient for a peer to reproduce the essential content. For licensed data (NielsenIQ, Compustat, CRSP, Census), provide access instructions and the linking/build code rather than raw data. Keep a master script regenerating every table, figure, and counterfactual.
Execution bridge (StatsPAI / Stata MCP)
Run the battery, don't just enumerate it. 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.
- Many outcomes / specifications:
romano_wolf (step-down FWER) or
benjamini_hochberg — report the adjusted threshold.
- OVB sensitivity:
oster_delta / sensemakr.
- Inference:
wild_cluster_bootstrap (few clusters), twoway_cluster / conley;
multilevel data → cluster at the right level.
- Re-fit off one handle:
audit_result(result_id) lists the missing checks and the
exact suggest_function for each.
- Exhibits:
etable / did_summary_to_latex from the handle — no retyped numbers.
Keep the decisive checks in the body and the exhaustive battery in the appendix. See the
executed chain in the JF execution walkthrough.
Checklist
Anti-patterns
- Reporting point estimates with no identification or fit evidence.
- A counterfactual that holds firm behavior fixed when firms would re-optimize.
- Elasticities/margins that are economically implausible, unaddressed.
- "Code available on request" instead of a replication-ready package.
Evidence pass for Marketing Science
Use this as a second-pass capability check. First lock the demand/supply mechanism, fit evidence, and counterfactual decision margin; then test whether the manuscript addresses quantitative marketing reviewers who read the model through the managerial counterfactual it makes possible.
- 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.
- Neighbor test: 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 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
【Estimator】GMM / MLE-SMLE / SMM / Bayes; SEs + convergence
【Identification evidence】sensitivity / Monte Carlo / first stage
【Fit】in-sample + holdout; economic plausibility of estimates
【Counterfactual】policy re-solved; magnitude ± uncertainty; mechanism
【Robustness】specs/instruments/normalizations
【Replication】data+code package status (licensed-data handling)
【Next step】mksc-contribution-framing
