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Why Instrumental Variables Identify Causal Effects
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Difference-in-Differences
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Omitted Variable Bias
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Econometrics Concept Explanations (WHY)
Each item below is a one-sentence, exam-ready explanation. Live WHY pages are linked; proposed WHYs are included for academic completeness and future expansion.
Causal Inference & Identification
- Econometrics: Why fixed effects reduces omitted bias — Fixed effects remove time-invariant unobserved heterogeneity that would otherwise correlate with regressors and bias estimates.
- (proposed) Why does endogeneity break causal interpretation? — Because regressors correlate with the error term, violating exogeneity and biasing estimates.
- (proposed) Why does identification require quasi-random variation? — Only exogenous variation isolates causal effects from confounding.
- (proposed) Why do DAGs clarify identification assumptions? — They make exclusion, backdoor paths, and confounding visually explicit.
- (proposed) Why does the potential outcomes framework define causal effects? — It compares outcomes under treatment vs control for the same unit.
- (proposed) Why do bad controls bias causal estimates? — Conditioning on post-treatment variables opens backdoor paths.
- (proposed) Why does selection bias distort causal inference? — Treated and control groups differ in unobserved ways that affect outcomes.
- (proposed) Why does internal validity matter more than external validity? — Without internal validity, the estimate is not causal anywhere.
Instrumental Variables (IV & 2SLS)
- (proposed) Why does an instrument need relevance? — Weak correlation with the endogenous regressor produces imprecise and biased IV estimates.
- (proposed) Why does an instrument need exogeneity? — If the instrument affects the outcome except through the regressor, exclusion fails.
- (proposed) Why does 2SLS recover causal effects under valid instruments? — First-stage isolates exogenous variation; second-stage maps it to outcomes.
- (proposed) Why do weak instruments bias IV toward OLS? — Weak first-stage strength amplifies finite-sample bias toward the endogenous estimator.
- (proposed) Why does overidentification allow testing exclusion restrictions? — Extra instruments provide redundant moment conditions that can be checked for consistency.
- (proposed) Why does LATE describe IV’s causal interpretation? — IV identifies the treatment effect for compliers affected by the instrument.
- (proposed) Why does the reduced form matter in IV? — It shows how the instrument shifts the outcome directly.
Difference-in-Differences (DiD)
- (proposed) Why does DiD require parallel trends? — Without parallel pre-trends, treated and control groups evolve differently even without treatment.
- (proposed) Why do event studies complement DiD? — They visualize dynamic treatment effects and test pre-trend assumptions.
- (proposed) Why does staggered adoption complicate DiD? — Later-treated units become controls for earlier-treated units, distorting comparisons.
- (proposed) Why does TWFE DiD fail with heterogeneous treatment effects? — TWFE averages incompatible comparisons across cohorts.
- (proposed) Why does the Goodman-Bacon decomposition diagnose DiD bias? — It decomposes TWFE estimates into weighted 2×2 comparisons.
- (proposed) Why do Sun & Abraham correct staggered DiD bias? — They estimate cohort-specific treatment effects without forbidden comparisons.
- (proposed) Why does Callaway & Sant’Anna improve DiD identification? — It constructs group-time ATT estimates that respect cohort timing.
Panel Data (FE, RE, Dynamic Panels)
- (proposed) Why does random effects require zero correlation between regressors and unobserved effects? — Correlation violates RE assumptions and biases estimates.
- (proposed) Why does the Hausman test choose between FE and RE? — It checks whether RE’s exogeneity assumption holds.
- (proposed) Why do dynamic panels require special estimators? — Lagged dependent variables correlate with fixed effects, violating standard assumptions.
- (proposed) Why does Nickell bias affect dynamic FE models? — The lagged dependent variable is mechanically correlated with the FE-transformed error.
- (proposed) Why do clustered standard errors matter in panel data? — They account for within-unit correlation over time.
- (proposed) Why does two-way FE control for unit and time shocks? — It removes both time-invariant unit effects and common shocks.
Regression Discontinuity (RDD)
- (proposed) Why does RDD identify causal effects at the cutoff? — Units just above and below the threshold are locally comparable.
- (proposed) Why does bandwidth choice matter in RDD? — Too wide introduces bias; too narrow increases variance.
- (proposed) Why do manipulation tests validate RDD? — If units sort around the cutoff, local randomization fails.
- (proposed) Why does fuzzy RDD require IV logic? — Treatment probability jumps at the cutoff, not treatment itself.
Time Series (ARIMA, VAR, Stationarity)
- (proposed) Why does stationarity matter in time series? — Nonstationary data produce spurious correlations and invalid inference.
- (proposed) Why do ARIMA models capture persistence and shocks? — They combine autoregression, differencing, and moving averages to model dynamics.
- (proposed) Why does differencing remove stochastic trends? — Differencing eliminates unit roots and stabilizes the mean.
- (proposed) Why do VAR models capture dynamic interactions? — Each variable responds to lags of all variables, modeling joint evolution.
- (proposed) Why does cointegration allow meaningful long-run relationships? — Cointegrated series share a stable equilibrium despite individual nonstationarity.
- (proposed) Why does the error correction model link short-run and long-run dynamics? — Deviations from equilibrium adjust future changes.
Specification, Diagnostics & Inference
- (proposed) Why does heteroskedasticity invalidate standard errors? — Variance of the error term changes across observations, violating OLS assumptions.
- (proposed) Why do robust standard errors fix heteroskedasticity? — They adjust the variance estimator without changing coefficients.
- (proposed) Why does autocorrelation bias inference in time series? — Errors correlate across time, understating uncertainty.
- (proposed) Why do HAC standard errors correct autocorrelation? — They adjust for serial dependence in the error structure.
- (proposed) Why does model misspecification bias estimates? — Incorrect functional form or omitted variables distort coefficients.
- (proposed) Why does cross-validation help with model selection? — It evaluates predictive performance on unseen data.
Econometrics Textbooks
Common texts used in graduate econometrics, empirical methods, causal inference, and time-series courses.
Core Econometrics Textbooks
Causal Inference and Applied Empirical Work
Panel Data and Microeconometrics
Econometrics Courses in California and Online Graduate Programs
Below are representative econometrics-focused courses common in California-area graduate programs and online graduate study.
Graduate Econometrics Courses
- UC Berkeley — Econometrics I — OLS, inference, and causal interpretation.
- UCLA — Econometrics I — regression, panel data, and IV methods.
- USC — Econometrics — estimation theory and applied modeling.
- Stanford — Econometric Methods — regression and empirical methods.
Applied Causal Inference and Policy Evaluation
- UC Berkeley — Applied Econometrics — treatment effects, DiD, IV, and policy analysis.
- UCLA — Program Evaluation Methods — quasi-experimental design and empirical policy work.
- USC — Causal Inference in Economics — identification strategies and robustness checks.
Panel Data and Time Series Courses
- UC Berkeley — Time Series Econometrics — ARIMA, VAR, and dynamic macro/finance applications.
- UCLA — Panel Data Econometrics — fixed effects, random effects, and dynamic panel models.
- Stanford — Applied Time Series — forecasting and serial dependence in economic data.
Online Graduate Programs
- Liberty — Applied Econometrics — regression and forecasting in graduate economics coursework.
- SNHU — Applied Econometrics — regression and data-driven modeling.
- Purdue Global — Econometrics and Forecasting — output interpretation and predictive modeling.
- GCU — Data Analysis and Econometric Methods — modeling and software-based interpretation.
Econometrics Video Lessons
Short walkthroughs covering regression, panel data, IV, causal inference, and time series.