Supervised Learning in Financial Econometrics

This lecture covers supervised learning in financial econometrics, focusing on linear regression and model fitting. Topics include the assumptions of linear models, model training, residuals, and potential problems like non-linearity, correlation of error terms, and heteroskedasticity. It also discusses basis functions, the bias-variance trade-off, subset selection methods, cross-validation, regularization techniques like ridge regression and Lasso, and the concept of the random forest algorithm.