As an empirical economist you will have to implement statistical techniques. There are many different programmes that can support you in this. Some of these (like STATA) are very specialised to economics. Two very popular programming languages that are used in this field are R and Python.
Learning any programming language can only be done by doing. Here we are not exposing you to a bottom-up introduction of R, but we are immediately exposing you to the exciting possibilities of R and the beauty and excitement of working with real-life data. As we do so we expose you to the basic tools of working with data in R and help you to learn these.
However, you have to accept from the outset that this is going to be a bumby raod! The most crucial skill you will have to embrace and develop is that of finding solutions to problems. You will, as any programmer, yes, even the most experienced ones, search the internet for solutions to your problem. Don’t think that you can remember all the important commands from the top of your mind.
Whenever we introduce a new R functionality we will briefly explain
it, but importantly we will also give you links to further resources
where you can find more usage examples and details. Also do not forget
to use the build-in help function in R (type ?FUNCTIONNAME
into the Console and press Enter). This is important as it is the help
that will be available offline as well.
In the following a few basic data techniques are introduced. Where you need to download csv files you will be guided to a github page from where you can download the respective csv file.
| Topic | Workthrough | Data used |
|---|---|---|
| Installing R and RStudio | Video | none |
| First Steps | Workthrough in R, Video | STATS19_GM_AccData.csv |
| Using Packages/Libraries | Workthrough in R | none |
| Loading Data | Workthrough in R | mroz.csv, mroz.xlsx |
| Basic Data Analysis | Workthrough in R | mroz.csv |
| Basic Data Analysis (Tidyverse) | Workthrough in R | mlb1.csv |
| Basic Data Visualisation (ggplot) | Workthrough in R | mlb1.csv |
| Loops | Workthrough in R | in code upload |
| Law of Large Numbers and Central Limit Theorem | Workthrough in R | in code simulated data |
| Topic | Workthrough | Data used |
|---|---|---|
| Regression Model Selection | Workthrough in R | in code upload |
| Ridge and LASSO Regression | Workthrough in R | PSIDsmall.txt |
| Post Double Selection LASSO | Workthrough in R | in code upload |
| Cross-Validation | Workthrough in R | in code upload |
| Topic | Workthrough | Data used |
|---|---|---|
| Basic TS modeling | Workthrough in R | EUROSTATtimeseries.csv |
| GARCH modeling | Workthrough in R | in code data download |
On the Examples section of this page we collate empirical examples which can be used to help students understand a range of statistical and econometric techniques. This page benefits from a range of empirical examples which have been implemented in a range of books.
The examples from these books and others have been amended or re-written such that students can either use them directly or to facilitate adoption by lecturers.
You can find further resources here.