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Regular version of the site
Master 2022/2023

Econometrics (Advanced Level)

Category 'Best Course for Broadening Horizons and Diversity of Knowledge and Skills'
Category 'Best Course for New Knowledge and Skills'
Type: Compulsory course (Master of Business Analytics)
Area of studies: Finance and Credit
Delivered by: Практико-ориентированные магистерские программы факультета экономических наук
When: 1 year, 2, 3 module
Mode of studies: distance learning
Online hours: 20
Open to: students of one campus
Instructors: Elena V. Semerikova
Master’s programme: Магистр аналитики бизнеса
Language: English
ECTS credits: 6
Contact hours: 12

Course Syllabus

Abstract

The course will be a core one for the Banking Institute Master program “Financial Analyst”. The course is intended for studying during the first and the second semester of the Master level education. The course is a prerequisite for some both core and specialized courses of the curriculum. Because of study of material of the course, a student should master and be able to prove the basic facts of strict development of classical econometrics. She/he should also know main ideas of univariate and multivariable time-series analysis including Box-Jenkins approach, ARIMA (p, d, q) models, non-stationary time-series, unit root tests, co-integration, VAR and VECM.
Learning Objectives

Learning Objectives

  • The purpose of the course is to give students new and extended skills in both econometric tools and their application to contemporary economic problems. The main studying purpose of such topics is to clear understanding of econometric ideas, assumptions under which econometric approaches can be applied. The student should have skills of application of the indicated tools and methods to researches in problems of Micro-, Macroeconomics and Finance. The student should have knowledge and skills of “Econometrics” (Bachelor level) and a number of mathematical and statistical courses such that “Linear algebra”, “Statistics”, “Probability theory”.
Expected Learning Outcomes

Expected Learning Outcomes

  • • to estimate linear regression model • to estimate and interpret logarithmic and semi-logarithmic models • to choose correct functional form of the model • to detect and correct for multicollinearity • to detect and treat autocorrelatied disturbances
  • • to estimate binary choice models • to estimate models with censored and truncated dependent variables • to estimate basic panel data models, • to choose appropriate model specification: fixed effects, random effects • to estimate dynamic panel data models with GMM
Course Contents

Course Contents

  • OLS
  • Model Specification
  • Multicollinearity, Heteroskedasticity and Autocorrelation
  • Endogeneity and Instrumental Variables
  • Project Assignment 1
  • Maximum Likelihood and Models with Limited Dependent Variables
  • Time-series econometrics. Univariate time series
  • Time-series econometrics. Multivariate time series
  • Panel Data Analysis
  • Project Assignment 2
Assessment Elements

Assessment Elements

  • non-blocking Еженедельные тесты
  • non-blocking Project Assignment 1
  • non-blocking Project Assignment 2
Interim Assessment

Interim Assessment

  • 2022/2023 3rd module
    0.2 * Project Assignment 2 + 0.16 * Project Assignment 1 + 0.64 * Еженедельные тесты
Bibliography

Bibliography

Recommended Core Bibliography

  • Jeffrey M. Wooldridge. (2019). Introductory Econometrics: A Modern Approach, Edition 7. Cengage Learning.
  • Verbeek, M. (DE-588)170802655, (DE-576)164668535. (2012). A guide to modern econometrics / Marno Verbeek. Chichester: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edswao&AN=edswao.357323661

Recommended Additional Bibliography

  • Econometric Analysis, 7th ed., international edition, 1239 p., Greene, W. H., 2012

Authors

  • MAKEEVA NATALYA MIKHAYLOVNA
  • VOLKOVA KIRA YUREVNA