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).
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill mksc-data-analysis --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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
- When to trigger
- Estimate, then prove identification empirically
- Assess model fit before trusting counterfactuals
- Counterfactuals are the payoff
- Robustness
- Replication package (plan now, deposit on acceptance)
- Execution bridge (StatsPAI / Stata MCP)
- Checklist
- Anti-patterns
- Evidence pass for Marketing Science
- Output format
What does the mksc-data-analysis skill do?
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).
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill mksc-data-analysis --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Awesome-Journal-Skills, a repository with 909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.