The aim is to make methods in the field of statistics and econometrics more tangible and to improve there understanding.
There are different ways to achieve, e.g. by:
Maybe some of the content can also improve your understanding
I'm a phd student and a teaching and research assistant at chair of Statistics and Econometrics at the University of Hohenheim.
Furthermore, I'm a lecturer at the University for Applied science HfWU in Nürtingen.
I earned my M.Sc. in Economics and Finance at the University of Tübingen and my B.Sc. in Volkswirtschaftslehre at the University for Applied Science HfWU in Nürtingen.
Find an animated illustration of core concepts in statistics and econometrics such as:
Use the links below to get to the Start page and the Github repository:
The Github repostiory with templates for your own illustration can be found here:
Note, this project is part of the DeLLFi (Integrating digitalization along teaching, learning, and research) project of the University of Hohenheim and funded by Foundation for Innovation in University Teaching
Find some replication material for:
Juselius, K. (2006). The cointegrated VAR model: methodology and applications. Oxford University Press.
Use the links below to get to the Bookdown page and the Github repository:
Note, this is one of the best books about the empirical application of cointegrated VAR models in empirical macroeconomics. However, most of the procedures are implemented using the software package RATS and CATS.
Note also, the material provides some implementations using the free software package R. Some of the procedures can be replicated using the R package urca. However, these procedures have to be changed a little bit to replicate the methods and the results of the book.
Stock, J. H., & Watson, M. W. (2020). Introduction to econometrics. Pearson Education.
In particular of, Chapter 17.6, Forecasting with Many Predictors Using Dynamic Factor Models and Principal Components
Use the links below to get to the Github repository:
Note, this is a quite nice application of using the pseudo out-of-sample root mean forecasting error to asses the forecasting performance of different forecasting models.
Find the current releases of a collection of important macro variables:
Use the links below to get to the Start page and the Github repository:
Note, I use this material in my course "intermediate Macroeconomics" at the University for Applied Science in Nürtingen. Since the lecture is in German, most of the material is German as well.