Important: from 2020 the course is taught in English.
| Worth (Points) | Info | |
|---|---|---|
| Midterm I | 30 | 2020-03-10 |
| Midterm II | 30 | 2020-04-28 |
| Exam | 40 | 2020-06-12 (exam schedule) |
| EXTRA (additional) | 5 | Final solution submission: - 2020-05-30 |
| 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 |