Practical Econometrics

[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.


[PE II] 2020 Spring Semester

2018-06-21 16:53

RESULTS: [Link]


Announcements

  •  

Important: from 2020 the course is taught in English.

Grading Information

 Worth (Points)Info
Midterm I30~2020-03-10 vs. 2020-03-17 (TBD)
Midterm II30~2020-04-21 vs. 2020-04-28 (TBD)
Exam40TBA
EXTRA (additional)5Two dates for partial and final result submission:
- TBA For "(1) Data Preparation and Analysis"
- TBA For "(2) Model Specification and Inference + VaR"
Total100 + 5 (from EXTRA)Minimum of 45 for passing grade, from which at least 5 are from the exam.

Course Information

 TitleLink
1Course IntroductionFile
Setup R, Python and JupyterLab on MIF Linux computersLink
2Template 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

 

   
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

 

 TitleLink
Statistical data and their modelsLink
1Stationary time seriesLink
Review+
2Time series with trend and seasonality componentsTBA
3Time series with unit rootTBA
4Regressions with time lagsTBA
5Regressions with time series variablesTBA
6Multivariate models: Granger causality, VAR and VECM modelsTBA
7Endogeneity problemTBA
8Simultaneous equationsTBA
9Panel data modelsTBA
10Additional Topics in Econometrics and Machine Learning modellingTBA
Autoregressive Conditional Heteroskedasticity ModelsLink
R_code
P_code
EXTRA TASKTBA

 

 


Some Useful Links

 TitleLanguageLink
1Time Series Analysis and Its Applications: With R ExamplesEnglishHomepage (DL)
2Forecasting: Principles and PracticeEnglishLink
3PREVIOUS MATERIAL: R.Lapinskas, Practical Econometrics II. Time Series Analysis (Lecture Notes)EnglishLink
4PREVIOUS MATERIAL: Lapinskas, Practical: Econometrics II. Time Series Analysis (Computer Labs)EnglishLink
R. Leipus. Laiko eilutėsLithuanianLink

 





Lectures From The Previous (2019) Year

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.

 

 TitleLectureExampleTask
1 Statistical data and their modelsPDF--
2Stationary time seriesPDF_1;
PDF_2
.Rmd   || R_out
.ipynb || P_out
HTML
3Time series with trend and seasonality componentsPDF_1;
PDF_2
.Rmd   || R_out
.ipynb || P_out
HTML
4Time series with unit rootPDF_1;
PDF_2
PDF_3
.ipynb || R_out
.ipynb || P_out
HTML
5Regressions with time lagsPDF⇓⇓⇓⇓⇓⇓
6Regressions with time series variablesPDF.ipynb || R_out
.ipynb || P_out
sim.ipynb || R_sim_out
HTML
7Multivariate models: Granger causality, VAR and VECM modelsPDFR_out
R_summary
HTML
8Endogeneity problemPDF--
9Simultaneous equationsPDF--
10Panel data modelsPDFR_outHTML
11Additional Topics in Econometrics and Machine Learning modellingPDF--
EXTRA TASK-.Rmd   || R_out
.ipynb || P_out
HTML