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