Introduces the FIN-403 Econometrics course, emphasizing practical application of standard econometric models like Ordinary Least Squares (OLS) in economic and financial contexts.
Explores residential energy demand analysis, modeling, and forecasting, emphasizing the importance of understanding consumption patterns and forecasting future demand.
Explores the integration of machine learning into discrete choice models, emphasizing the importance of theory constraints and hybrid modeling approaches.
Covers the basics of linear regression, including OLS, heteroskedasticity, autocorrelation, instrumental variables, Maximum Likelihood Estimation, time series analysis, and practical advice.
Explores heteroskedasticity and autocorrelation in econometrics, covering implications, applications, testing methods, and hypothesis testing consequences.
Introduces the Generalized Method of Moments (GMM) in econometrics, focusing on its application in instrumental variable estimation and asset pricing models.
Covers the basics of Ordinary Least Squares (OLS) in econometrics, including variable relationships, coefficient determination, and model interpretation.
Explores heteroskedasticity in econometrics, discussing its impact on standard errors, alternative estimators, testing methods, and implications for hypothesis testing.
Explores supervised learning in financial econometrics, covering linear regression, model fitting, potential problems, basis functions, subset selection, cross-validation, regularization, and random forests.
