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.
| Title | Link | |
|---|---|---|
| 1 | Course Introduction, Overview and Requirements | Link, R_vs_Python |
| 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. | Python: JupyterLab (HTML output) R: JupyterLab (HTML output) R: RStudio (HTML output) |
| Title | Link | |
|---|---|---|
| 1 | Univariate Regression: General Concepts, OLS, R & Python implementation | Link |
| 2 | Univariate Regression: OLS, Regression Models & Interpretation | Link |
| 3 | Univariate Regression: MLE, Confidence Intervals and Hypothesis Testing | Link |
| 4 | Univariate Regression: Hypothesis Testing (Review), OLS Prediction and Prediction Intervals, GoF | Link |
| 📊 | Univariate Regression: Data Subsampling & Chapter Review | Link |
| 5 | Multivariable Regression: Model Specification | Link |
| 6 | Multivariable Regression: OLS, Confidence Intervals, Hypothesis Testing & GoF | Link |
| 7 | Multivariable Regression: Restricted Least Squares, Multicollinearity | Link |
| 8 | Multivariable Regression: Generalized Least Squares, Heteroskedastic and Autocorrelated Errors | Link |
| 9 | Multivariable Regression: General Modelling Difficulties | Part I Part II |
| 📊 | Multivariable Regression: Chapter R Code Review | Link |
| 10 | Discrete Response Models: Binary Response Variables - Logit, Probit & Parameter Interpretation | see lecture notes |
| 11 | Discrete Response Models: Binary Response Variables - Goodness-Of-Fit | see lecture notes |
| 12 | ||
| 13 | ||
| 14 | ||
| EXTRA TASK | Link |
| Title | Language | Link | |
|---|---|---|---|
| 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.