Structural Estimation

Original name

Original description

Use whenever an analysis estimates the PRIMITIVES of an economic model rather than a reduced-form relationship — preferences/utility, costs, information/consideration, search, or conduct — or whenever the question requires a COUNTERFACTUAL the data doesn't contain (a merger, a new product, a tax, a removed friction, consumer welfare/surplus, equilibrium re-pricing). Fires for structural demand estimation (logit, random-coefficients/BLP), supply-side markup-and-cost recovery, dynamic discrete choice (Rust/CCP), entry and dynamic games, auctions, limited consideration sets, and search models — estimated by GMM/method of (simulated) moments, NLS, or maximum (simulated) likelihood. Forces the structural workflow: justify going structural over reduced form, name what identifies each parameter, PROVE the algorithm recovers known parameters via Monte Carlo before trusting real data, derive analytical gradients group-by-group when the estimator admits them, re-solve equilibrium for counterfactuals with one scenario per mechanism. Use in R, Julia, or Python even when the user just says "estimate a demand model", "simulate the merger", "recover marginal costs", "what's the welfare effect", or "fit a structural model" — a converged optimizer is not an identified model, and a clean estimation run says nothing about whether the counterfactuals are right.