This course provides a practical approach to learning Econometrics and R and requires you to have followed the introductory part “Introduction to Econometrics with R”. You will learn about an important method to establish causal relationships in non-experimental data, called “Instrumental Variables”. You will learn about panel data, that is, data which tracks individuals over time. You will look at situations when our outcome data is discrete in nature, like “subject i chose option A (and not B).” And we will look at a range of simple machine learning methods which are helpful for classification and prediction tasks.

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Syllabus and Slides

Lecture 1: Introduction, Logistics and Recap 1 from intro course. Uncertainty in regression estimates, orthogonality of error, BLUE property. [HTML] [PDF]

Lecture 2: Recap 2 from intro course. What’s a model, omitted variable bias, interpreting coefficients, the log transformation [HTML] [PDF]

Lecture 3: Difference-in-Differences [HTML] [PDF]

Lecture 4: Instrumental Variables and Causality 1. John Snow’s Cholera Experiment as a motivation for the IV estimator, using a DAG to think about the exclusion restriction, the Wald estimator. [HTML] [PDF]

Lecture 5: Instrumental Variables and Causality 2. 2SLS, returns to schooling and ability bias, replicating Angrist and Krueger (1991), IV mechanics, identification and inference, weak instruments [HTML] [PDF]

Coming soon