Learning R for data analysis
- A gentle introduction to R
- Data wrangling and graphics with the tidyverse
- Simple and multiple regression in matrix form and using black box routines
- Inference in small samples and asymptotics
- Monte Carlo simulations
- Heteroscedasticity
- Time series regression
- Pooled cross-sections and panel data
- Instrumental variables and two-stage least squares
- Simultaneous equation models
- Limited dependent variables: binary, count data, censoring, truncation, and sample selection
- Formatted reports and research papers combining R with R Markdown or LaTeX
