Econometrics
Routing Summary
This folder covers applied econometrics and causal inference from Mostly Harmless Econometrics plus Bayesian DiD, synthetic control, DAG tutorials, Bayesian propensity weighting, simulation-based estimation, staggered difference-in-differences, high-dimensional dependence (copula) modelling, and quasi-Bayesian GMM under plausible (non-exact) moment conditions. Contains 53 notes across 7 sub-topics.
- Need research design fundamentals, selection bias, or DAGs? → Foundations
- Need regression interpretation or OVB? → Regression Foundations
- Need IV, DiD (canonical), RD, synthetic control, GSC, DAGs, or propensity weighting? → Identification Strategies
- Need staggered/multi-period DiD (group-time ATT, doubly-robust estimands, event-study aggregation, multiplier-bootstrap inference)? → Difference-in-Differences
- Need quantile regression, discrete choice, or SEs? → Extensions
- Need simulation-based estimation (MSM, indirect inference, EMM, copula SMM)? → Extensions
- Need high-dimensional dependence / copulas, tail dependence, or factor copulas? → Dependence Modeling
- Need quasi-Bayesian GMM with plausible (non-exact) moment conditions, priors over misspecification, or the “no free lunch” weighting trade-off? → Plausible GMM
Book Overview
- Mostly Harmless Econometrics - Overview — Master index for the book’s concepts and structure (moved to Identification Strategies/)
Sub-topics
| Sub-topic | Notes | Domain |
|---|---|---|
| Foundations | 4 | Research questions, experimental ideal, selection bias, DAGs (MHE Part I + Pearl) |
| Regression Foundations | 3 | CEF, CIA, omitted variables bias (MHE Ch 3) |
| Identification Strategies | 16 | IV, LATE, DD, RD, synthetic control, GSC, DAGs, Bayesian IPTW — quasi-experimental methods (MHE Ch 4-6 + Abadie 2021 + Xu 2017 + extras) |
| Difference-in-Differences | 6 | Staggered/multi-period DiD: group-time ATT(g,t), parallel-trends/no-anticipation/overlap assumptions, OR/IPW/doubly-robust estimands, event-study/group/calendar aggregation, multiplier-bootstrap uniform inference, TWFE critique (Callaway & Sant’Anna 2020) |
| Extensions | 23 | Quantile regression, discrete choice, standard errors (MHE Ch 7-8), simulation-based estimation: MSM, indirect inference, EMM, SMM for copulas, and foundational time-series SME theory (Liesenfeld & Breitung 1998, Evans 2024, Oh & Patton 2011, Duffie & Singleton 1993) |
| Dependence Modeling | 6 | High-dimensional copulas, factor-copula construction, tail dependence via EVT, multi-factor/block structures, rank-based SMM, S&P 100 & systemic risk (Oh & Patton 2012) |
| Plausible GMM | 5 | Quasi-Bayesian inference when moment conditions are plausible but not exact: plausibility characteristic , proper prior over misspecification, CU-GMM quasi-posterior, local Gaussian prior approximation & “no free lunch”, institutions-and-GDP IV application (Chernozhukov, Hansen, Kong & Wang 2026) |
Sources
- Mostly Harmless Econometrics.pdf — Full textbook PDF (Angrist & Pischke, 2008)
- Discrete Choice and Random Utility Models — PyMC tutorial: Bayesian discrete choice models (McFadden framework)
- Difference in differences — PyMC tutorial: Bayesian DiD with counterfactual prediction
- 15 - Synthetic Control — Causal Inference for the Brave and True — Causal Inference for the Brave and True, Ch. 15: synthetic control with Python (scipy, sklearn)
- Abadie 2021 - Using Synthetic Controls.pdf — Abadie (2021) JEL: authoritative guide to synthetic controls, bias theory, requirements, extensions
- Xu 2016 - Generalized Synthetic Control Method.pdf — Xu (2017) Political Analysis: GSC method unifying DID and SC via IFE model
- Unlock the Secrets of Causal Inference with a Master Class in Directed Acyclic Graphs — Graham Harrison, Towards Data Science (2023): comprehensive DAG tutorial
- How to use Bayesian propensity scores and inverse probability weights — Andrew Heiss (2021-12-18): Liao-Zigler Bayesian IPW in R/brms
- 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”
- 19. Simulated Method of Moments Estimation — Computational Methods for Economists using Python — Evans (2024), CompMethods Ch. 19: full Python SMM tutorial + Brock-Mirman structural macro exercise
- Oh-Patton-2012-Factor-Copulas.pdf — Oh & Patton (2012), “Modelling Dependence in High Dimensions with Factor Copulas” (Duke): factor copulas, EVT tail dependence, rank-based SMM, S&P 100 systemic risk
- 1803.09015-Callaway-SantAnna-DiD-Multiple-Periods.pdf — Callaway & Sant’Anna (2020), “Difference-in-Differences with Multiple Time Periods” (J. Econometrics): group-time ATT, doubly-robust estimands, aggregation schemes, multiplier-bootstrap inference, minimum-wage application
- Plausible GMM - A Quasi-Bayesian Approach — Chernozhukov, Hansen, Kong & Wang (2026), arXiv:2507.00555 (econ.EM): quasi-Bayesian GMM under plausible (non-exact) moment conditions, priors over misspecification, Bernstein–von Mises concentration, institutions-and-GDP IV application
- Duffie Singleton 1993 - Simulated Moments Estimation of Markov Models of Asset Prices — Duffie & Singleton (1993), Econometrica 61(4):929–952: foundational Simulated Moments Estimator (SME) theory for time-series Markov asset-pricing models — geometric ergodicity, AUC condition, consistency, asymptotic normality
See Also
- Bayesian Statistics — Bayesian perspective on regression and inference
- Research Methodology — Multiple comparisons and causal inference challenges