This course examines the theoretical foundations and practical applications of widely used econometric techniques. It begins with an examination of classical regression methods for estimating socio-economic relationships. Topics include regression in econometric modeling, model errors, specification issues, indicator/dummy variables, dynamic models, nonlinear models, and limited dependent variables. Additionally, it covers the identification and estimation of systems of equations, Error Correction Models and Vector Autoregressive Models. Students will gain hands-on experience by applying these techniques using a general econometrics software package to analyze real-world economic and business datasets. Prerequisite: Admission to the Master of Science in Data Science program or approval of the instructor.
This course examines the theoretical foundations and practical applications of widely used econometric techniques. It begins with an examination of classical regression methods for estimating socio-economic relationships. Topics include regression in econometric modeling, model errors, specification issues, indicator/dummy variables, dynamic models, nonlinear models, and limited dependent variables. Additionally, it covers the identification and estimation of systems of equations, Error Correction Models and Vector Autoregressive Models. Students will gain hands-on experience by applying these techniques using a general econometrics software package to analyze real-world economic and business datasets. Prerequisite: Admission to the Master of Science in Data Science program or approval of the instructor.
