Individual skill
mksc-data-analysis
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).
Marketing-Science-Skills/skills/mksc-data-analysis/SKILL.md
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# 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.