(3 units). The classical model of multiple linear regression. Relaxation of the classical least-squares assumptions: autocorrelation, heteroscedasticity and multicollinearity. Generalized least-squares estimation. Simultaneous equation models: foundation, specification, identification, and estimation. Indirect least-squares and two-stage least squares methods of estimation. Distributed-lag models. Dummy variables. Pooling cross-section and time-series data. Course Component: Lecture
(3 units). The classical model of multiple linear regression. Relaxation of the classical least-squares assumptions: autocorrelation, heteroscedasticity and multicollinearity. Generalized least-squares estimation. Simultaneous equation models: foundation, specification, identification, and estimation. Indirect least-squares and two-stage least squares methods of estimation. Distributed-lag models. Dummy variables. Pooling cross-section and time-series data. Course Component: Lecture
