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  GoodnessOfFit  see lecture notes 
12  
13  
14  
EXTRA TASK  Link 
Title  Language  Link  

1  NIST/SEMATECH eHandbook 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.
Important: from 2020 the course is taught in English.
Worth (Points)  Info  

Midterm I  30  ~20200310 vs. 20200317 (TBD) 
Midterm II  30  ~20200421 vs. 20200428 (TBD) 
Exam  40  TBA 
EXTRA (additional)  5  Two dates for partial and final result submission:  TBA For "(1) Data Preparation and Analysis"  TBA For "(2) Model Specification and Inference + VaR" 
Total  100 + 5 (from EXTRA)  Minimum of 45 for passing grade, from which at least 5 are from the exam. 
Title  Link  

1  Course Introduction  File 
Setup R, Python and JupyterLab on MIF Linux computers  Link  
2  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) 
Practical Econometrics Lecture Notes: PE II (Spring semester) only. Contains the exercises and examples, will eventually contain the notes from the slides.  Link  
Practical Econometrics Lecture Notes: PE I (Fall semester) only. Will be eventually combined with PE II.  Link 
Title  Link  

Statistical data and their models  Link  
1  Stationary time series  Link Review+ 
2  Time series with trend and seasonality components  TBA 
3  Time series with unit root  TBA 
4  Regressions with time lags  TBA 
5  Regressions with time series variables  TBA 
6  Multivariate models: Granger causality, VAR and VECM models  TBA 
7  Endogeneity problem  TBA 
8  Simultaneous equations  TBA 
9  Panel data models  TBA 
10  Additional Topics in Econometrics and Machine Learning modelling  TBA 
Autoregressive Conditional Heteroskedasticity Models  Link R_code P_code 

EXTRA TASK  TBA 
Title  Language  Link  

1  Time Series Analysis and Its Applications: With R Examples  English  Homepage (DL) 
2  Forecasting: Principles and Practice  English  Link 
3  PREVIOUS MATERIAL: R.Lapinskas, Practical Econometrics II. Time Series Analysis (Lecture Notes)  English  Link 
4  PREVIOUS MATERIAL: Lapinskas, Practical: Econometrics II. Time Series Analysis (Computer Labs)  English  Link 
R. Leipus. Laiko eilutės  Lithuanian  Link 
Slides from ~topic 5 are expected to change the most in the 2020 updated course.
Example code will also be updated and moved to the lecture notes to clearly distinguish optional information, as well as easier R and Python comparison in the 2020 updated course.
Again, when preparing for the course – use the updated slides in the “Lecture Files” section and only evaluate the table below for a rough estimate of the lessons to come.
Title  Lecture  Example  Task  

1  Statistical data and their models      
2  Stationary time series  PDF_1; PDF_2  .Rmd  R_out .ipynb  P_out  HTML 
3  Time series with trend and seasonality components  PDF_1; PDF_2  .Rmd  R_out .ipynb  P_out  HTML 
4  Time series with unit root  PDF_1; PDF_2 PDF_3  .ipynb  R_out .ipynb  P_out  HTML 
5  Regressions with time lags  ⇓⇓⇓  ⇓⇓⇓  
6  Regressions with time series variables  .ipynb  R_out .ipynb  P_out sim.ipynb  R_sim_out  HTML  
7  Multivariate models: Granger causality, VAR and VECM models  R_out R_summary  HTML  
8  Endogeneity problem      
9  Simultaneous equations      
10  Panel data models  R_out  HTML  
11  Additional Topics in Econometrics and Machine Learning modelling      
EXTRA TASK    .Rmd  R_out .ipynb  P_out  HTML 