Namų darbų užduotys (atnaujinamos semestro eigoje): [LINK]
Rezultatai: [LINK]
Įdomumui: Bloomberg: “Reddit’s Stock Threads Become a Must-Read on Wall Street”
This is the page for the course Practical Econometrics with R and Python 2020 Autumn Semester. Lectures are held remotely via MS Teams.
Link to the lecture notes (usually updated during the course): [LINK]
This page, as well as the one on moodle, will be updated throughout the course.
Namų darbai Nr. 4: 4.1; 4.2; 4.4; 7.1 (a) + (b) + (c)
Namų darbai Nr. 3: 3.7; 3.12; 3.15
Vertinimai įkelti.
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 | Part I Part II |
| 📊 | Multivariable Regression: Chapter R Code Review | Link |
| 10 | Discrete Response Models: Binary Response Variables - Logit, Probit & Parameter Interpretation | see lecture notes |
| 11 | Discrete Response Models: Binary Response Variables - Goodness-Of-Fit | see lecture notes |
| 12 | ||
| 13 | ||
| 14 | ||
| EXTRA TASK | Link |
| Title | Language | Link | |
|---|---|---|---|
| 1 | NIST/SEMATECH e-Handbook 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) |
| Econometrics website, literature, dataset links, etc. | Lithuanian | Link | |
| Terminology and overview of descriptive statistics and probability theory | Lithuanian | Link | |
| Račkauskas A., Įvadas į ekonometriją (Paskaitų konspektai) | Lithuanian | Link | |
| Čekanavičius V. ir Murauskas G., Statistika ir jos taikymai, II knyga | Lithuanian | MIF library | |
| Čekanavičius V. ir Murauskas G., Statistika ir jos taikymai, III knyga | Lithuanian | MIF 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.
Vertinimai paskelbti.
Užduočių sprendimų ataskaitas siųsti: andrius.buteikis@mif.vu.lt
Užduočių sprendimai turi būti atsiųsti dvejais formatais:
## Užduotis 9.8
<Kodas duomenų užkrovimui>
### (a)
<Kodas (9.8) užduoties (a) dalies sprendimui>
### (b)
<Kodas (9.8) užduoties (b) dalies sprendimui>
## Užduotis 9.9
<Kodas duomenų užkrovimui>
ir t.t.
Užduotys: (1.1), (1.2), (1.3).
(1.1) Užduotis: failas “d-3stocks9908.txt“:
P11 <- read.table("http://faculty.
(1.2) Užduotis: failas “m-gm3dx7508.txt“:
P12 <- read.table("http://faculty.
P13 <- P12
Užduotys: (2.4); (2.5) + išnagrinėti |r_t|, (r_t)^2 koreliacijas bei Teiloro Efektą; (2.10)
(2.4) Užduotis: failas “m-deciles08.txt“:
P21 <- read.table("http://faculty.
(2.5) Užduotis: failas “d-ibm3dx7008.txt“:
P22 <- read.table("http://faculty.
+ Papildomai ištirti: |r_t| ir (r_t)^2 koreliacijas ir Taylor’o efektą.
P23A <- read.table("http://faculty.chicagobooth.edu/ruey.tsay/teaching/fts3/w-Aaa.txt", as.is = TRUE, header = FALSE)P23B <- read.table("http://faculty.chicagobooth.edu/ruey.tsay/teaching/fts3/w-Baa.txt", as.is = TRUE, header = FALSE)Užduotys: (3.5); (3.6); (3.12).
(3.5) Užduotis: failas “m-intc7308.txt“:
P31 <- read.table("http://faculty.
(3.6) Užduotis: failas “m-mrk4608.txt“:
P32 <- read.table("http://faculty.
(3.12) Užduotis: failas “d-gmsp9908.txt“:
P33 <- read.table("http://faculty.
Užduotys: (3.8); (4.1); (4.2).
(3.8) Užduotis: failas “m-gmsp5008.txt“:
P41 <- read.table("http://faculty.chicagobooth.edu/ruey.tsay/teaching/fts3/m-gmsp5008.txt", as.is = TRUE, header = TRUE)
(4.1) Užduotis: failas “d-jnj9808.txt“:
P42 <- read.table("http://faculty.chicagobooth.edu/ruey.tsay/teaching/fts3/d-jnj9808.txt", as.is = TRUE, header = TRUE)
(4.2) Užduotis: failas “m-ge2608.txt“:
P43 <- read.table("http://faculty.
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 |