Practical Econometrics I / II

[PE I] 2020 Fall Semester

2020-09-13 18:18

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.

[PE I] 2019 Fall Semester

2019-08-17 10:27




  • Extra Task(-s): results added (see above file);
  • Midterm I: results added (see above file);
  • Midterm II: results added (see above file);
  • Exam: results added (see above file). Grades will be added in the system on Saturday, around 14:00.

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.


Course Information

Important: from 2019 the course is taught in English.

1Course Introduction, Overview and RequirementsLink,
Setup R, Python and JupyterLab on MIF Linux computersLink
2Lecture Notes (usually updated throughout the course)Link
3Template 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.
Python: JupyterLab
(HTML output)

R: JupyterLab
(HTML output)

R: RStudio
(HTML output)


Lecture Files

1Univariate Regression:
General Concepts, OLS, R & Python implementation
2Univariate Regression:
OLS, Regression Models & Interpretation
3Univariate Regression:
MLE, Confidence Intervals and Hypothesis Testing
4Univariate Regression:
Hypothesis Testing (Review), OLS Prediction and Prediction Intervals, GoF
📊Univariate Regression:
Data Subsampling & Chapter Review
5Multivariable Regression:
Model Specification
6Multivariable Regression:
OLS, Confidence Intervals, Hypothesis Testing & GoF
7Multivariable Regression:
Restricted Least Squares, Multicollinearity
8Multivariable Regression:
Generalized Least Squares, Heteroskedastic and Autocorrelated Errors
9Multivariable Regression:
General Modelling Difficulties
Part I
Part II
📊Multivariable Regression:
Chapter R Code Review
10Discrete Response Models:
Binary Response Variables - Logit, Probit & Parameter Interpretation
see lecture
11Discrete Response Models:
Binary Response Variables - Goodness-Of-Fit
see lecture


Some Useful Links

1NIST/SEMATECH e-Handbook of Statistical MethodsEnglishLink
2Principles Of Econometrics with REnglishLink
3Using R for Introductory EconometricsEnglishLink
4An Introduction to Statistical Learning with Applications in REnglishLink (Homepage)
5The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd Ed.)EnglishLink (Homepage)
Econometrics website, literature, dataset links, etc.LithuanianLink
Terminology and overview of descriptive statistics and probability theoryLithuanianLink
Račkauskas A., Įvadas į ekonometriją (Paskaitų konspektai)LithuanianLink
Čekanavičius V. ir Murauskas G., Statistika ir jos taikymai, II knygaLithuanianMIF library
Čekanavičius V. ir Murauskas G., Statistika ir jos taikymai, III knygaLithuanianMIF 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.