Content
The course is organised in height three-hours sessions. Each session articulates a theoretical lecture and practical applications using the R language. After each class, you will be expected to prepare exercises that will be corrected during the next class. The sessions are organised as follows:
| Session | Topic | Duration |
|---|
| 1 | Multiple regression | 3 hours |
| 2 | Introduction to R | 3 hours |
| 3 | Causality and inference | 3 hours |
| 4 | Specification and selection | 3 hours |
| 5 | Heteroscedasticity | 3 hours |
| 6 | Autocorrelation | 3 hours |
| 7 | Endogeneity | 3 hours |
| 8 | Other estimation issues | 3 hours |
All lectures, exercises, solutions and resources will be made available on the Dropbox of the course. You should come to class with your computer with administrator rights, as well as a working internet connection. Make sure to install the latest stable release of R, available on the CRAN website for all platforms. In addition, I strongly recommend that you install an IDE / GUI such as RStudio, also available for all platforms on their website.
Grading
Final test (50%)
A computer-based test will be held at the end of the semester during one of the practice sessions. You will be given a dataset with a set of questions to perform applied analysis including estimation, hypothesis testing, the detection and correction of estimation issues, and inference.
Individual report (50%)
You will have to submit a short report (10 pages maximum) by the end of the semester, along with a script reproducing your results. You will investigate a research question of your choice using some of the techniques studied in class. You may use Rmarkdown to weave together text, code and output. Grading is based on the following criteria:
| Criteria | Description |
|---|
| Question | Research question of interest to economists |
| Background | Short review of the related literature |
| Data | Data collection and construction of the datasets |
| Model | Choice of the appropriate model and specification |
| Estimation | Appropriate diagnostics and corrections |
| Results | Interpretation and answer to the research question |
| Writing | Clear and concise. English, referencing, etc. |
