Repository
This repository provides materials for the course "Statistics II: Time Series, Panel Data and Limited Dependent Variables (with R)". You'll find code and data used in the course:
- The
codefolder features R scripts used in class as well as assignment scripts and solutions (provided with some delay): - The
datafolder features various datasets used in class.
FAQ
1. I'm a Stata, not an R guy, but want to learn R. Where to start?
- Swirl - Learn R interactively with the swirl package. Link
- Datacamp's Learn R (any much more) online. Some courses are free. Link
- UCLA Data Analysis Examples with R. Link
- R for Data Science (Wickham and Grolemund) - free online book on modern statistical analysis with R. Link.
2. How can I efficiently get the files from the archive on my local drive?
- The quick way: click on the green "Clone or download" button in the upper-right corner; then select "Download ZIP". The entire archive will be downloaded as a ZIP file. Just unzip it and work with the folder you just downloaded.
- The clean and more sustainable way: Install GitHub Desktop, available for download here. Clone the repository and pull the data whenever new data are available. To get a deeper understanding of Git/GitHub, you should check out these tutorials/slides: Link 1, Link 2.
3. How do I complete my assignments and hand them in?
- The assignments for each session are provided in an RMarkdown file, e.g., "01-r-primer-assignment.Rmd". RMarkdown files are best opened with RStudio. (Some packages might have to be installed to work with RMarkdown files.)
- After having opened the assignment file with RStudio, save it as a new file with the following naming convention:
0X-solutions-prename-surname.Rmd, e.g.:01-solutions-simon-munzert.Rmd. - Now you can start working on the assignment. Do so by adding code and comments if necessary.
- When you are done with your assignment, klick on the
Knitbutton and selectKnit to HTML. This should produce an HTML file with both R code and printed output. - Finally, upload the HTML file to Moodle.
Note: Be sure to check out the files 01-r-primer-assignment.Rmd, 01-r-primer-solutions.Rmd, and 01-r-primer-solutions.html as an example.
To be continued.
Course contents
This course introduces students to an array of commonly used statistical techniques with a strong emphasis on actual application. All classes take place in the computer lab and divide time between theory and application. Students are assigned a problem set at the end of each class covering that day’s materials and the beginning of the following class is used to review the answers. As this is an applied class, most of these assignments involve the proper analysis of practice datasets using R. This class design is intended to provide students with both a theoretical and concrete understanding of statistical techniques. The course begins with a review of OLS regression and covers elementary time‐series and panel data, before introducing students to maximum likelihood estimation and some categorical data designs.
Instructor