Extensions
Routing Summary
This folder covers extensions to the core econometric toolkit: three standalone method notes (MHE Ch. 7–8 + PyMC) plus three sub-topics (simulation-based estimation, copula SMM, BLP demand). Contains 23 notes across 4 areas.
- Need distributional effects or QTE? → Quantile Regression
- Need multinomial logit/probit or random utility? → Discrete Choice Models
- Need robust SEs, clustering, or Moulton factor? → Standard Errors and Clustering
- Need random-coefficients logit / BLP demand estimation (contraction mapping, GMM instruments, PyBLP)? → BLP Demand Estimation
- Need simulation-based estimation (MSM, indirect inference, EMM, weighting, Python code)? → Simulation-Based Estimation
- Need SMM applied to copulas (Oh & Patton: theory, testing, application)? → Copula SMM
Sub-topics
| Sub-topic | Notes | Covers |
|---|---|---|
| BLP Demand Estimation | 6 | Random-coefficients logit demand, the BLP contraction mapping, GMM estimation with instruments for price endogeneity, numerical integration/optimization, supply side & markups — Conlon & Gortmaker (2020), PyBLP |
| Simulation-Based Estimation | 15 | General MSM/SMM/indirect inference/EMM theory and implementation — Liesenfeld & Breitung (1998), Evans (2024) — plus the foundational time-series SME theory (geometric ergodicity, AUC condition, consistency, asymptotic normality) — Duffie & Singleton (1993) |
| Copula SMM | 5 | SMM for copula models: dependence measures, estimator, asymptotic theory, J-test, Monte Carlo — Oh & Patton (2011) |
Standalone Notes
- Quantile Regression — CONTAINS: Conditional quantile functions, quantile treatment effects (QTE), approximation property, distributional effects
- Discrete Choice Models — CONTAINS: Random utility model, multinomial logit/probit, IIA assumption, Bayesian discrete choice in PyMC, McFadden framework
- Standard Errors and Clustering — CONTAINS: Heteroskedasticity-robust SEs, Moulton factor, serial correlation in panels, few-cluster corrections, wild bootstrap
Sources
- Mostly Harmless Econometrics.pdf — Mostly Harmless Econometrics (Angrist & Pischke, 2008), Chapters 7–8
- Discrete Choice and Random Utility Models — PyMC tutorial: Bayesian discrete choice models
- tdb136.pdf — Liesenfeld & Breitung (1998), “Simulation Based Methods of Moments in Empirical Finance”
- Oh_Patton_SMM_copulas_nov11.pdf — Oh & Patton (2011), “Simulated Method of Moments Estimation for Copula-Based Multivariate Models”
- Computational Methods for Economists — Ch. 19 — Evans (2024)
- Duffie Singleton 1993 - Simulated Moments Estimation of Markov Models of Asset Prices — Duffie & Singleton (1993), Econometrica 61(4):929–952
See Also
- Generalized Linear Models — Bayesian approach to logistic/Poisson regression
- Monsters and Mixtures — Maximum entropy justification for categorical models
- Copula Estimation — Bayesian copula estimation (complementary approach to Copula SMM)