Second course in the PhD sequence in econometrics at the Economics Department (as Econ ECON 271) and at the GSB (as MGTECON ECON 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.
3-5 units · Letter or Credit/No Credit
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.
Offered in Winter 2027 at Stanford University.