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Econometrics I

Academic Year
Instruction in English
ECTS credits
Course type:
Compulsory course
3 year, 1, 2 module


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

  • to know how the research questions can be solved using econometrics
  • to learn how to calculate linear regression coefficients
  • to know Gauss-Markov Theorem
  • to be able to detect heteroscedasticity problem
  • to know how to interpret coefficients of the linear regression model
  • to learn how to calculate multivariable regression coefficients
  • be able to detect the multicollinearity problem
  • to be aware of the consequences of the omitted variable bias
  • be able to interpret interaction effects and squared terms
  • to learn how to describe and incorporate qualitative variables into the regression models
  • 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 learn how to estimate the model with the binary variable
Course Contents

Course Contents

  • Introduction
    Students learn what econometrics is. In addition, we discuss typical research questions are used in econometric studies. Finally, we consider basic problems with data management.
  • Review of Probability
    This lecture reviews the core ideas of the theory of probability that are needed to understand regression analysis and econometrics
  • The Linear Regression Model: an Overview
    This topic is about the OLS estimator. We study why we use OLS estimator. Moreover, we consider the population and sample regression. In addition, we learn how to calculate linear regression coefficients and interpret the results.
  • The Gauss-Markov Theorem.
    This topic is about assumptions of the linear regression model. We discuss why these assumptions are important for the model. In addition, we consider what if the homoscedasticity assumption cannot be held in our study.
  • Multiple Regression Analysis
    This topic considers the problem of the omitted variable. The possible solution to this problem is to use the variable (or proxy) that has been omitted in the model before. Thus, we turn to the multivariate regression model. We discuss how to calculate coefficients of the multivariable model and learn one of the most common problems of these models: multicollinearity.
  • Multiple Regression Analysis: tests
    In this topic we discuss how to verify if the multivariable model shows reliable results. We consider several basic tests that we use in the multivariable regression model.
  • Overview of multiple regression
    This material brings together several fundamental and empirical issues in multiple regression analysis
  • Regression analysis with qualitative information
    This topic covers almost all of the popular ways that qualitative independent variables are used in cross-sectional analysis, as well as the interactions among dummy variables
  • Multicollinearity, Heteroskedasticity and Relaxing the Assumptions of the Classical Model
    The topic discusses the two common and potential problems in regression analyses- Multicollinearity and Heterosckedasticity that prevent correct estimation of standard errors and can consequently lead to erroneous hypothesis tests about the significance of predicted coefficients. Also, we discuss some other typical problems in empirical research.
  • Specification and data issues. Case example.
    This topic discusses functional form misspecification, measurement errors, missing data, nonrandom sample, outliers, and solving the data case.
  • Binary dependent variable II
    Logit and probit models. Estimation and interpretation
  • Binary dependent variable
    Students learn how to estimate a regression model with the binary dependent variable. Maximum Likelihood estimation of logit and probit.
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

  • Interim assessment (2 module)
    0.2 * Home assignments + 0.5 * Lab work + 0.3 * Test


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., Hill, R.C., 2011