Important: from 2020 the course is taught in English.
Worth (Points)  Info  

Midterm I  30  20200310 
Midterm II  30  20200428 
Exam  40  20200612 (exam schedule) 
EXTRA (additional)  5  Final solution submission:  20200530 
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  Link Additional 
3  Time series with unit root  Link Part II Additional 
4  Regressions with time lags  Link 
5  Regressions with time series variables  Link TASKS Example 
6  Multivariate models: Granger causality, VAR and VECM models  Link Summary Unit Root & Cointegration TASKS Example (R) Example (Python, incomplete) 
7  Endogeneity problem  [SKIPPED] 
8  Simultaneous equations  [SKIPPED] 
9  Panel data models  Link TASKS Example (R) 
10  Additional Topics in Econometrics and Machine Learning modelling  TBA 
Autoregressive Conditional Heteroskedasticity Models  Link R_code P_code 

EXTRA TASK  Link 
Title  Language  Link  

1  Introduction to Time Series and Forecasting  English  Homepage 
2  Time Series Analysis and Its Applications: With R Examples  English  Homepage (DL) 
3  Forecasting: Principles and Practice  English  Link 
4  PREVIOUS MATERIAL: R.Lapinskas, Practical Econometrics II. Time Series Analysis (Lecture Notes)  English  Link 
5  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 