3.0

credits

Average Course Rating

Builds upon the statistical methods introduced in Econometrics I. Expands upon linear regression and causal inference models to account for non-random treatment assignment, selection bias, structural estimation, and nonparametric estimation. Expands the econometric toolkit built in Econometrics I, which focused primarily on econometric methods for estimation, to applications involving time series data, forecasting, and econometric methods for prediction. Aims to improve students’ ability to conduct economic analysis using observational data. Relies on exercises designed to provide hands-on experience in structuring, coding, and interpreting econometric models with applications to global public health. Employs tools and methods and compares the results to those obtained from initial estimations based on very restricted assumptions.