Second course in the PhD sequence in econometrics at the Economics Department (as Econ MGTECON 271) and at the GSB (as MGTECON 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 MGTECON 270 / MGTECON MGTECON 603.
4 units · GSB Student Option LTR/PF
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.
Offered in Winter 2027 at Stanford University.