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  Link 
📊  Multivariable Regression: General Modelling Difficulties  
10  Discrete Response Models: Binary Response Variables  Logit, Probit & Parameter Interpretation  
11  Discrete Response Models: Binary Response Variables  GoodnessOfFit  
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) 
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.
Rezultatai: [RESULTS]
Some interesting links:
Worth (Points)  Info  

Midterm I  30  20190319 
Midterm II  30  ~ 20190423 
Exam  40  TBA 
EXTRA (additional)  5  Two dates for partial and final result submission:  20190412 For "(1) Data Preparation and Analysis"  20190521 For "(2) Model Specification and Inference" 
Total  100 + 5 (from EXTRA)  Minimum of 45 for passing grade. 
Title  Link  

1  Course Introduction  File 
2  Python Notebook Example  File & Output 
3  R Notebook Example  File & Output 
4  Literature (Theory)  R.Lapinskas, Practical Econometrics II. Time Series Analysis (Lecture Notes) 
5  Literature (Practice)  R.Lapinskas, Practical Econometrics II. Time Series Analysis (Computer Labs) 
Practical Econometrics Lecture Notes (PE I only. Will be combined with PE II at the end of the course)  Link  
Practical Econometrics Lecture Notes (PE II only, updated after each topic)  Link 
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 
Title  Language  Link  

1  R. Leipus. Laiko eilutės  Lithuanian  Link 
2  Time Series Analysis and Its Applications: With R Examples  English  Link 
Rezultatai: [RESULTS], [EXTRA_TASK_RESULTS]
Title  Link  

1  Course Introduction  Introduction 
2  Literature (Theory)  R.Lapinskas, Practical Econometrics I. Regression Models (Lecture Notes) 
3  Literature (Practice)  R.Lapinskas, Practical Econometrics I. Regression Models (Computer Labs) 
[(LINK) A First Lesson In Econometrics (2 pages)]
Title  Link  

1  Lecture Notes (updated for each lecture)  Link 
2  Univariate Regression Tasks  Link 
Univariate Regression Example with wage dataset Other Univariate Regression Examples  [HTML (R)] [.ipynb] [.7zip] Link 

3  Multiple Regression Tasks (updated for each lecture)  Link 
4  Multiple Regression Example with wage dataset  [HTML (Python)], [.zip], [.ipynb] [HTML (R)], [.ipynb] 
5  Discrete Response Model Tasks  Link 
6  Discrete Response Model Example with labor force participation dataset  [HTML (R)], [.ipynb] 
7  EXTRA TASK  Link 
Title  Language  Link  

1  Econometrics website, literature, dataset links, etc.  Lithuanian  Link 
2  A. Račkauskas. Įvadas į ekonometriją (Paskaitų konspektai)  Lithuanian  Link 
3  Terminology and overview of descriptive statistics and probability theory  Lithuanian  Link 
4  NIST/SEMATECH eHandbook of Statistical Methods  English  Link 
5  Principles Of Econometrics with R  English  Link 
6  Using R for Introductory Econometrics  English  Link 
To use CramervonMises test in Python:
pip install scikitgof
import skgof as skgof
skgof.cvm_test(data = my_model.resid, dist = stats.norm(0, np.sqrt(np.var(my_model.resid))))[1]