investment-education

Learn how instrumental variables and two-stage least squares (2SLS) solve endogeneity in econometric models. Covers instrument validity, weak instruments, testing, and the LATE interpretation.

Learn panel data analysis: fixed effects, random effects, the Hausman test, pooled OLS, first differencing, and two-way fixed effects. Covers within transformation, GLS estimation, clustered standard errors, and practical decision rules for applied finance research.

Learn how difference-in-differences isolates causal effects from natural experiments by comparing treatment and control groups before and after a policy change.

Learn about cointegration, unit root tests and error correction models for analyzing long-run equilibrium relationships in finance.

Learn how dummy variables encode qualitative data in regression models, including intercept shifts, multiple categories, interaction terms, the Chow test for structural breaks, and the dummy variable trap.

Learn what omitted variable bias is, how to sign the direction of bias, and how multicollinearity, measurement error, and proxy variables affect regression estimates. Includes the OVB formula, VIF thresholds, and a comparison of solutions.

Learn how serial correlation affects regression analysis — detection with Durbin-Watson and Breusch-Godfrey tests, correction with Newey-West HAC standard errors and Cochrane-Orcutt/Prais-Winsten FGLS.

Time series regression models dynamic relationships in financial and economic data. Learn what makes time series special, how to handle trends and seasonality, the role of stationarity, and the adapted OLS assumptions for valid inference.

Learn what heteroskedasticity is, why it biases standard errors but not OLS coefficients, how to detect it with the Breusch-Pagan and White tests, and how to fix it with robust standard errors or weighted least squares.

Econometrics applies statistical methods to economic and financial data to estimate relationships, test theories, and evaluate decisions. Learn the four data types, the empirical process, and why establishing causality is the central challenge.