Dummy Variables Regression: Categorical Effects Explained
Dummy variables regression brings categorical data into a numeric model. Learn how to build dummies, avoid the dummy variable trap,...
Econometrics
Unlock the power of econometrics with insights on key models and tools that help explain and forecast economic trends. Perfect for both beginners and advanced researchers.
Interaction Terms Regression: When Effects Depend on Context
Interaction terms regression lets the effect of one variable depend on another. Learn how to add product terms, read the...
Spurious Regression: When Trends Create False Relationships
Spurious regression makes unrelated trending series look strongly connected, with high R-squared and large t-statistics. Learn why it happens, how...
Weak Instruments: Why IV Estimates Become Unreliable
Weak instruments make IV estimates unreliable by dragging two-stage least squares back toward biased OLS. Learn the first-stage F diagnostic,...
Omitted Variable Bias: Missing Variables Distort Estimates
Omitted variable bias explains how missing correlated variables distort regression estimates and weaken causal interpretation in econometric models.
Generalized Method of Moments: Unified Estimation Framework
The generalized method of moments turns model-implied orthogonality conditions into a unified estimation framework that nests OLS, instrumental variables, and...
Heckman Selection Model: Correcting Sample Selection Bias
The Heckman selection model corrects for sample selection bias in wage equations and any setting where the outcome is observed...
Hausman Test: Choosing Between Fixed and Random Effects
The Hausman test compares fixed effects and random effects estimators in panel data, deciding which set of assumptions about unobserved...
Maximum Likelihood Estimation: Choosing the Parameters That Best Explain the Data
Maximum likelihood estimation chooses parameters that make the observed data most probable. This guide explains the method, its properties, applications,...
GARCH model: Volatility Clustering in Markets
GARCH model estimation explains volatility clustering in S&P 500 returns through conditional variance, likelihood iteration, persistence, and risk-desk applications.