Basics of econometrics

2020/2021

Academic Year

ENG

Instruction in English

3

ECTS credits

Delivered at:

Department of Management

Course type:

Bridging course

When:

1 year, 1 module

Instructors

Antipov, Evgeny A.

Pokryshevskaya, Elena B.

Chermoshentsev, Evgenii

Course Syllabus

Abstract

The course introduces students to basic statistical methods for exploratory analysis and the analysis of relationships. Students will get ready for data management and for basic comparative, correlation and regression analysis of survey, sales and other types of data commonly used in marketing and management. Students will get intermediate R statistical computing skills necessary for more advanced courses. 50% of the course is dedicated to hands-on R coding, which is why the course will be useful even for students who studied Econometrics using another package but want to master the popular and powerful R package. In addition, being tailored to management students, this course does not duplicate traditional Econometrics courses for economists in terms of empirical examples used in it.

Learning Objectives

• Choose methods adequately corresponding to the objectives of a research project
• Collect, store, process and analyze data according to high standards
• Conduct empirical research in management and marketing using modern analytic software tools
• Explore new data to extract actionable insights
• Model relationships between outcomes and their drivers
• Produce dynamic reproducible research reports with publication-quality tables and graphics

Expected Learning Outcomes

• Choose methods adequately corresponding to the objectives of a research project
• Collect, store, process and analyze data according to high standards
• Solve economic and managerial problems using best practices of data analysis using modern computational tools
• Ability to import and preprocess data to statistical software
• Conduct descriptive analysis of data
• Explore and test relationships between various features
• Explore differences among subgroups of data
• Test differences among groups of data
• Identify drivers of outcomes

Course Contents

• Introduction. Review of basic probability and statistics concepts.
• Introduction to the R Language
• Describing Data
• Relationships between Continuous Variables
• Comparing Groups: Tables and Visualizations
• Comparing Groups: Statistical Tests
• Identifying Drivers of Outcomes: Linear Models. Regression diagnostics.

Assessment Elements

• Class activity
• Kahoot
• Midterm Exam
• Exam

Interim Assessment

• Interim assessment (1 module)
  0.25 * Class activity + 0.25 * Exam + 0.25 * Kahoot + 0.25 * Midterm Exam

Bibliography

Recommended Core Bibliography

• Chapman, C., & Feit, E. M. (2019). R For Marketing Research and Analytics (Vol. Second edition). Cham: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2093001
• Hamid Seddighi. (2012). Introductory Econometrics : A Practical Approach. Routledge.

Recommended Additional Bibliography

• Greenberg, E. (2013). Introduction to Bayesian Econometrics: Vol. 2nd ed. Cambridge eText.