Econometrics

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.

Learn simple linear regression and the OLS method. Understand the population model, slope and intercept interpretation, R-squared, and key assumptions with finance examples.

Learn how to test regression coefficients using t-tests, F-tests, p-values, and confidence intervals, with worked finance examples and the key distinction between statistical and economic significance.

Learn how to choose and interpret regression functional forms including log-level, level-log, log-log, quadratic, and interaction term specifications in econometric analysis.

Multiple regression analysis estimates the effect of each independent variable on Y while holding all others constant. Learn OLS estimation with multiple regressors, R-squared vs adjusted R-squared, the Gauss-Markov assumptions, and ceteris paribus interpretation with finance examples.