This is the page for the course Practical Econometrics with R and Python 2020 Autumn Semester. Lectures are held remotely via MS Teams.
Link to the lecture notes (usually updated during the course): [LINK]
This page, as well as the one on moodle, will be updated throughout the course.
Important! Download the following [.ipynb file] file and make sure that everything works on your machine.
ERASMUS students, who want to use R + RStudio, can use RStudio and verify that everything works by downloading the [.Rmd] file and inputting their student code and pressing the “Knit” button in RStudio to generate a .html file.
Important: from 2019 the course is taught in English.
|1||Course Introduction, Overview and Requirements||Link,
|Setup R, Python and JupyterLab on MIF Linux computers||Link|
|2||Lecture Notes (usually updated throughout the course)||Link|
|3||Template files on how to separately write down various formulas, as well as format text/comments alongside your code blocks/chunks and its output. |
The code is provided only as a simple example to see how it visually looks like alongside some random comments.
General Concepts, OLS, R & Python implementation
OLS, Regression Models & Interpretation
MLE, Confidence Intervals and Hypothesis Testing
Hypothesis Testing (Review), OLS Prediction and Prediction Intervals, GoF
|📊||Univariate Regression: |
Data Subsampling & Chapter Review
|5||Multivariable Regression: |
OLS, Confidence Intervals, Hypothesis Testing & GoF
Restricted Least Squares, Multicollinearity
Generalized Least Squares, Heteroskedastic and Autocorrelated Errors
General Modelling Difficulties
Chapter R Code Review
|10||Discrete Response Models: |
Binary Response Variables - Logit, Probit & Parameter Interpretation
|11||Discrete Response Models: |
Binary Response Variables - Goodness-Of-Fit
|1||NIST/SEMATECH e-Handbook of Statistical Methods||English||Link|
|2||Principles Of Econometrics with R||English||Link|
|3||Using R for Introductory Econometrics||English||Link|
|4||An Introduction to Statistical Learning with Applications in R||English||Link (Homepage)|
|5||The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd Ed.)||English||Link (Homepage)|
|Econometrics website, literature, dataset links, etc.||Lithuanian||Link|
|Terminology and overview of descriptive statistics and probability theory||Lithuanian||Link|
|Račkauskas A., Įvadas į ekonometriją (Paskaitų konspektai)||Lithuanian||Link|
|Čekanavičius V. ir Murauskas G., Statistika ir jos taikymai, II knyga||Lithuanian||MIF library|
|Čekanavičius V. ir Murauskas G., Statistika ir jos taikymai, III knyga||Lithuanian||MIF library|
Note: in the past, there were free DataCamp courses available (as introductions to R and Python programming languages). Unfortunately, as of around Q1 of 2019 this is no longer the case.
Consequently, the lecture notes are updated to include links to alternative freely available interactive courses for R (using `swirl` package) and Python (using PyCharm Edu) programming languages.