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Course Syllabus

2022/2023

Econometrics (Advanced Level)

Category 'Best Course for New Knowledge and Skills'
Type: Mago-Lego
Delivered by: School of Economics and Finance
When: 1-3 module
Open to: students of one campus
Instructors: Evgeniya Shenkman, Grigorii Teplykh
Language: English
ECTS credits: 9
Contact hours: 84

Course Syllabus

Full SyllabusAsk Question

Abstract

The course «Advanced econometrics» is designed for first-year graduate master students following the program «Finance». Its main goal is to familiarize the students with advanced methods of econometric research, and their application to finance area using the appropriate software. The important accent is made on the selection of adequate econometric methods and program tools for the solution of research problems which could arise during the analysis of financial markets.
Learning Objectives

Learning Objectives

  • Providing a theoretical knowledge about state-of-the-art econometrical methods of data analysis.
  • Forming practical skills of application of econometrical methods
  • Developing of skills of work with specialized statistical software.
Expected Learning Outcomes

Expected Learning Outcomes

  • Students knows and uses advanced econometric methods
  • Students knows the theoretical base of econometrics and basic methods of analysis
  • Student has the skills to work with statistical software, required to analyze the data.
Course Contents

Course Contents

  • 1.1. Simple linear regression
  • 1.2. Multiple linear regression
  • 1.3. Violation of assumptions in linear regression models
  • 2.1. Stationary Time Series.
  • 2.2. Nonlinearities in mean.
  • 2.3. Nonlinearities in variance.
  • 2.4. State space models, introduction.
  • 2.5. Basic principles of forecasting.
  • 3.1. Nonlinear models
  • 3.2. Endogeneity
  • 3.3. Panel data models
  • 3.4. Endogeneity in panel data.
Assessment Elements

Assessment Elements

  • non-blocking Project 1
    Research using econometrics on a topic of limited choice. Performed individually or in small groups
  • non-blocking Seminar activities 1
    Work at the seminars includes a detailed analysis of the theory and training with statistical software. Testing takes place every day of the seminars. There are three components of the final grade of seminars: attendance (30-40%); average grade of testing (40-50%); and answering the open questions (20-30%). The concrete grading structure depends on the current level of the audience.
  • non-blocking Exam 1
    Writing offline exam in 70-80 minutes. It consists of 8-10 test questions and 4-5 open tasks.
  • non-blocking Seminar activities 2
    After each seminar session (in the second module) students prepare a report of performed tasks in a written form. Each written report is assessed on a 10-point scale with 10 points for correctly and accurately performed tasks. If not, the grade will be smaller proportional to the number of mistakes or omitted tasks. The overall grade for seminar activities calculated as an average grade of all reports.
  • non-blocking Exam 2
    The exam is a test for 60 minutes on Smart LMS platform (edu.hse.ru). The exam covers topics only on Time Series Analysis (part 2 of the course)
  • non-blocking Self-study work 2
    During the course, students perform tasks on DataCamp platform to master programming skills in R. The grade for datacamp tasks is calculated as follows. Each task fulfillment before the deadline gives 2 points, each task fulfillment after deadline gives 1 point. The whole grade is calculated as a sum and then converted to a 10 point scale.
  • non-blocking Seminar activities 3
    Work at the seminars includes a detailed analysis of the theory and training with statistical software. Testing takes place every day of the seminars. There are three components of the final grade of seminars: attendance (30-40%); average grade of testing (40-50%); and answering the open questions (20-30%). The concrete grading structure depends on the current level of the audience.
  • non-blocking Project 3
    Research using econometrics on a topic of limited choice. Performed individually or in small groups
  • non-blocking Exam 3
    Writing offline exam in 70-80 minutes. It consists of 8-10 test questions and 4-5 open tasks.
Interim Assessment

Interim Assessment

  • 2022/2023 1st module
    0.3 * Project 1 + 0.4 * Exam 1 + 0.3 * Seminar activities 1
  • 2022/2023 2nd module
    0.1 * Self-study work 2 + 0.5 * Seminar activities 2 + 0.4 * Exam 2
  • 2022/2023 3rd module
    0.3 * 2022/2023 1st module + 0.1 * Project 3 + 0.3 * 2022/2023 2nd module + 0.1 * Seminar activities 3 + 0.2 * Exam 3
Bibliography

Bibliography

Recommended Core Bibliography

  • Introductory econometrics : a modern approach [Lecture notes on econometrics 2], Wooldridge J.M., 2012
  • Tsay, R. S. (2010). Analysis of Financial Time Series (Vol. 3rd ed). Hoboken, N.J.: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=334288
  • Tsay, R. S. (2013). An Introduction to Analysis of Financial Data with R. Wiley.
  • Путеводитель по современной эконометрике : учеб.-метод. пособие, Вербик М., Айвазян С.А., 2008
  • Эконометрика : учебник : пер. с англ., Хайяши Ф., 2017

Recommended Additional Bibliography

  • Econometric analysis of cross section and panet data, Wooldridge J.M., 2002
  • Introductory econometrics : a modern approach [Lecture notes on econometrics 1], Wooldridge J.M., 2012
  • Microeconometrics using stata, Cameron A.C., Trivedi P.K., 2010

Authors

  • Борисова Елена Феликсовна
  • TEPLYKH GRIGORIY VASILEVICH
  • Shenkman Evgeniia Andreevna