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Econometrics I

Учебный год
Обучение ведется на английском языке
Курс обязательный
Когда читается:
3-й курс, 1, 2 модуль


Аверьянова Юлия Вадимовна

Альбаева Венера Ильшатовна

Course Syllabus


This course provides students with skills in basic econometrics analysis for management and economics studies. The course covers the theoretical aspect of linear and discrete choice models. These models are the most popular ones in econometrics analysis for management and economics studies, and they are frequently used for empirical term papers and bachelor theses. In sum, the course provides a balanced study of applied and theoretical aspects of econometrics, all of which are necessary for basic econometric analysis.
Learning Objectives

Learning Objectives

  • The objective of this course is to provide students with the basic knowledge of econometrics. Studies will learn the regression models for cross-sectional data and cover the theory and its applications in economics or management or finance. After successful completion of the course, students will be able to formulate an econometric model and use regression analysis for assessing relationships among variables.
Expected Learning Outcomes

Expected Learning Outcomes

  • be able to detect the multicollinearity problem
  • be able to interpret interaction effects and squared terms
  • to be able to detect heteroscedasticity problem
  • to be aware of the consequences of the omitted variable bias
  • to collect, organise, and analyse data, as well as interpret results from statistical analyses
  • to construct, test, and analyse econometric models, using variables and relationships commonly found in the studies of economic and management theory
  • to know Gauss-Markov Theorem
  • to know how the research questions can be solved using econometrics
  • to know how to interpret coefficients of the linear regression model
  • to learn how to calculate linear regression coefficients
  • to learn how to calculate multivariable regression coefficients
  • to learn how to describe and incorporate qualitative variables into the regression models
  • to learn how to estimate the model with the binary variable
Course Contents

Course Contents

  • Introduction
  • The Linear Regression Model: an Overview
  • The Gauss-Markov Theorem.
  • Multiple Regression Analysis
  • Review of Probability
  • Multiple Regression Analysis: tests
  • Overview of multiple regression
  • Regression analysis with qualitative information
  • Multicollinearity, Heteroskedasticity and Relaxing the Assumptions of the Classical Model
  • Assessing studies based on multiple regression
  • Binary dependent variable
  • Binary dependent variable II
Assessment Elements

Assessment Elements

  • non-blocking Home assignments
    Rounding is arithmetic. We inform you about scores at the end of Module 1 and in accordance with the official deadline, we inform you about the final mark and the score for modules 1 and 2.
  • non-blocking Test
  • non-blocking Lab work
Interim Assessment

Interim Assessment

  • 2021/2022 2nd module
    0.3 * Test + 0.2 * Home assignments + 0.5 * Lab work


Recommended Core Bibliography

  • Stock, J. H., & Watson, M. W. (2015). Introduction to Econometrics, Update, Global Edition (Vol. Updated third edition). Boston: Pearson Education. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1419285

Recommended Additional Bibliography

  • An introduction to modern econometrics using Stata, Baum, C., 2006
  • Brooks,Chris. (2019). Introductory Econometrics for Finance. Cambridge University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.b.cup.cbooks.9781108422536
  • Introductory econometrics: a modern approach, Wooldridge, J.M., 2009
  • Using Stata for principles of econometrics, Adkins, L.C., 2011