# Advanced Econometrics, Sciences Po (2022-2023)

*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.*

## 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]

**Lecture 6**: Panel Data: What, How and Why? Application to crime rates at county level, within and between variation, the within transformation, running panel regressions in R [HTML] [PDF]

**Lecture 7**: Discrete Outcomes: Logit and Probit. Bernoulli reminder, Mroz dataset, the linear probability model, the saturated LPM, logit and probit marginal effects, Goodness of fit in non-linear binary response models. [HTML] [PDF]

**Lecture 8**: Intro to Statistical (or Machine) Learning 1: the bias-variance-tradeoff, taxonomy of methods, parametric vs non-parametric, linear vs nonlinear, relationship between variance, bias and MSE [HTML] [PDF]

**Lecture 9**: Intro to Statistical (or Machine) Learning 2: Subset selection (Lasso and Ridge regressions), unsupervised learning (PCA and K-means clustering) [HTML] [PDF]

# Public Economics for Public Policy (MPA), Sciences Po (2022-2023)

*This course focuses on the role of the government in the economy. The aim is to provide students with a comprehensive understanding of the economic principles and tools necessary to analyze and evaluate government interventions in the economy. Through the exploration of theoretical models and empirical methods, students will gain the ability to assess the benefits and consequences of various public policies. The course covers tax policy and inequality, tax evasion and avoidance, social insurance programs, public goods, externalities, and environmental protection. While the primary focus will be on the US and Europe, the course will also incorporate insights from international experiences.*

## Slides

**Lecture 1**: Introduction to Public Economics [PDF]

**Lecture 2**: Tools of Public Finance [PDF]

**Lecture 3**: Taxation, Externalities, and Climate Change [PDF]

**Lecture 4**: Capital Taxation & Tax Havens [PDF]

**Lecture 5**: Social Insurance [PDF]

**Lecture 6**: Education [PDF]