EMPIRICAL METHODS 2   [Archived Catalog]
2020-2021 Graduate & Professional Studies Catalog


Minimum Credits: 3
Maximum Credits: 3
This course will cover methods to cope with selection bias and external validity (e.g.: imperfect IV, plausibly exogenous IV, weighting matrix), decomposition methods, multiple hypothesis testing, methods for spatial analysis (ARGIS), causal mediation analysis, an introduction to machine learning and big data in economic research, discrete choice models and dynamic choice model applications. Many examples will come from development, health, labor, public economics, and political economy, but the material will be useful to any applied researcher. The course will focus also on the implementation of econometric techniques learning the basic tools of programming and coding. We will use STATA, ARCGIS, R, and Matlab. The goal of this class is to provide students with the tools needed to become critical readers of empirical work and teach them techniques that they can apply to their own original research.
Academic Career: Graduate
Course Component: Lecture
Grade Component: Grad LG/SNC Basis

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