# [PE II] 2020 Spring Semester

2018-06-21 16:53

## Announcements

• The results and final grades added. The results will also appear in the system either on the 23rd, or the 24th.

Important: from 2020 the course is taught in English.

Worth (Points)Info
Midterm I302020-03-10
Midterm II302020-04-28
Exam402020-06-12 (exam schedule)
- 2020-05-30
Total100 + 5 (from EXTRA)Minimum of 45 for passing grade, from which at least 5 are from the exam.

## Course Information

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.
Practical Econometrics Lecture Notes: PE I (Fall semester) only.
Will be eventually combined with PE II.

Review+
2Time series with trend and seasonality componentsLink
Part II
Example
6Multivariate models: Granger causality, VAR and VECM modelsLink
Summary
Unit Root & Cointegration
Example (R)
Example (Python, incomplete)
7Endogeneity problem[SKIPPED]
8Simultaneous equations[SKIPPED]
Example (R)
10Additional Topics in Econometrics and Machine Learning modellingTBA
R_code
P_code

1Introduction to Time Series and ForecastingEnglishHomepage
2Time Series Analysis and Its Applications: With R ExamplesEnglishHomepage (DL)
4PREVIOUS MATERIAL: R.Lapinskas, Practical Econometrics II. Time Series Analysis (Lecture Notes)EnglishLink
5PREVIOUS MATERIAL: Lapinskas, Practical: Econometrics II. Time Series Analysis (Computer Labs)EnglishLink

### 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.

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--