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Regular version of the site
Bachelor 2020/2021

## Econometrics I

Area of studies: Management
When: 3 year, 1 module
Mode of studies: offline
Language: English
ECTS credits: 4

### Course Syllabus

#### Abstract

This course provides students with skills in basic econometrics analysis for management studies. In addition, the course covers the theoretical aspect of linear and discrete choice models. These models are the most popular ones in econometrics analysis for management 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

• The objective of this course is to provide students with the basic knowledge of econometrics. Studies will learn the linear regression model theory and its applications in economics or management or finance.

#### Expected Learning Outcomes

• to know what research questions can be solved using econometrics
• to learn to calculate linear regression coefficients
• to know how to interpret coefficients of the linear regression model
• to know Gauss-Markov Theorem
• to be able to detect heteroscedasticity problem
• 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 run the test of the joint significance of multiple coefficients
• be able to interpret interaction effects and squared terms
• be able to export STATA outputs to MS Word
• to know what we have to write in econometric research

#### 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.
• 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.
• Relaxing the Assumptions of the Classical Model
In this topic we discuss a real empirical reseach to show how the linear regression models works in practice. We discuss the problem of endogeneity and some other typical problems. In addition, what we have to write in an empirical research report.

• Test 1
• Test 2
• Exam

#### Interim Assessment

• Interim assessment (1 module)
0.6 * Exam + 0.2 * Test 1 + 0.2 * Test 2

#### 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