Econometric Methods II

4 credits · GSB

Second course in the PhD sequence in econometrics at the Economics Department (as Econ 271) and at the GSB (as MGTECON 604). This course presents modern econometric methods with a focus on panel regression, machine learning, and time series. Among the topics covered are: estimation and linear regression recap; panel data methods including differences in differences, event studies, fixed-effect models, synthetic control; machine learning methods including supervised and unsupervised learning; uses of machine learning as a tool in econometrics and causal inference; statistical decision theory including econometrics with misaligned preferences; time-series models including state-space models and dynamic stochastic general equilibrium models. Prerequisites: This course assumes working knowledge of basic probability theory, statistics, econometrics, and causal inference as covered in Econ 270 / MGTECON 603.