Elements of Econometrics 2020/21

About the course

The Elements of Econometrics is an introductory full-year course for the 3-rd year DSBA students. The course relies on Dougherty’s EC2020 University of London (UoL) and Econometrics course at ICEF adapted for CS students. The course is taught in English and examined by the University of London international programme. The stress in the course is done on the essence of statements, methods and approaches of econometric analysis. The conclusions and proofs of basic formulas and models are given which allows the students to understand the principles of econometric theory development. The main attention is paid to the economic interpretations and applications of the econometric models. The first part of the course is devoted to the cross-section econometrics; the second part – to the time series and panel data econometrics.

Course prerequisites: Statistics, Mathematics for Economists, Introduction to Economics.

Lecturers and Teachers

Group	181	182
Lecturer	Igor Kheifets
Classteacher	Ksenia Kasianova
Assistants	Christina Golubeva Kristina Krinitsyna Ayna Arrykova

Methods of Instruction

The following methods and forms of study are used in the course:

• lectures (2 hours a week);
• classes (2 hours a week, there are combined theoretical and applied analysis and prac- tical applications of the econometric methods studied in the course);
• home assignments;
• the applied essay (Semester 2);
• teachers’ consultations;
• self study, which can be conducted with the course materials and in a computer room, making home assignments using econometric software, work with economic data bases, with; FCS, UoL, and other course materials;
• online studies using UoL Virtual Learning Environment information systems, as well as other online resources: lecture videos, webinars, online tasks and con- tests.

Grading

G=0.4∗(0.25∗Goct +0.5∗Gdec +0.25∗Gha1)+0.1∗Gha2 +Gessay +0.5Gfin +Gbonus,

where Gha1 and Gha2 – the grades for home assignments in semester 1 and 2, Gfin – the grade for final exam of the University of London, Goct and Gdec – the grades for October and December exams out of 100, Gessay – bonus points for essay (out of 10), Gbonus – bonus points for class participation and contests.

Reading

Main Reading:
[1] Christopher Dougherty. Introduction to econometrics (4th or 5th ed.) Oxford University Press, 2011. url: https://global.oup.com/uk/orc/busecon/economics/dougherty5e/student/.
[2] Christopher Dougherty. Elements of econometrics. Study Guide. University of London., 2016.url: https://emfss.elearning.london.ac.uk/pluginfile.php/121361/mod_label/intro/EC2020-vle.pdf.

Supplementary reading:
[3] Joshua D Angrist and Jo¨rn-Steffen Pischke. Mostly harmless econometrics: An empiricist’s companion. Princeton university press, 2008.
[4] Peter Kennedy. A guide to econometrics (6th ed. or earlier). MIT press, 2008.
[5] Damodar N Gujarati and Dawn C Porter. Basic econometrics (5th ed.) Singapore: McGrew Hill Book Co, 2009.
[6] William H Greene. Econometric Analysis (7th ed. or earlier). Prentice Hall, 2012.
[7] Marno Verbeek. A guide to modern econometrics (4th ed. or earlier). John Wiley & Sons, 2012.
[8] James H Stock and Mark W Watson. Introduction to econometrics. 2015.
[9] Jeffrey M Wooldridge. Introductory econometrics: A modern approach. Nelson Education, 2016.
[10] RStudio Team. RStudio: User’s Manual. RStudio, PBC. Boston, MA, 2020. url: https://www.r-studio.com/downloads/Recovery_Manual.pdf.

Online resources:
[11] R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. Vienna, Austria, 2017. url: https://www.R-project. org/.
[12] RStudio Team. RStudio: Integrated Development Environment for R. RStudio, PBC. Boston, MA, 2020. url: http://www.rstudio.com/.
[13] University of London. UoL Portal. url: https://my.london.ac.uk/.
[14] Oxford University Press. Dougherty: Introduction to Econometrics 5e. url: https: //global.oup.com/uk/orc/busecon/economics/dougherty5e/.
[15] Data source. Bank of Russia. url: http://www.cbr.ru.
[16] Data source. The Russian Federal State Statistics Service. url: http://www.gks.ru.