Practical Econometrics I / II

[PE I] 2019 Fall Semester

2019-08-17 10:27

RESULTS: [Link]

 


Announcements

  • 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.

 TitleLink
1Course Introduction, Overview and RequirementsLink,
R_vs_Python
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

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

 


Some Useful Links

 TitleLanguageLink
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.