mksc-data-analysis - SKILL.md Agent Skill

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

Estimator run; appropriate SEs and convergence diagnostics reported
Identification shown empirically (sensitivity/Monte Carlo/first stage)
In-sample fit and (where possible) holdout/out-of-sample validation
Estimates economically plausible (elasticities, margins, discount factor)
Counterfactual re-solves equilibrium; magnitudes with uncertainty + mechanism
Robustness to specification/instruments/normalization
Replication package (code + data access/build) prepared

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