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Econometrics I

2021/2022
Учебный год
ENG
Обучение ведется на английском языке
6
Кредиты
Статус:
Курс обязательный
Когда читается:
2-й курс, 3, 4 модуль

Преподаватели

Аверьянова Юлия Вадимовна

Course Syllabus

Abstract

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

• 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

• 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

• Introduction
• Review of Probability
• The Linear Regression Model: an Overview
• The Gauss-Markov Theorem.
• Multiple Regression Analysis
• 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
• Overview of the course. Research project.

Assessment Elements

• Home assignments
Rounding is arithmetic. Students will be informed about scores at the end of Module 3 and Module 4
• Test 1
This is a closed-book test (the use of any materials is prohibited). The answers on each task have a fixed cost in points. The cost of a specific question corresponds to its complexity and the estimated time and effort that the student must spend to answer it. On open questions, bonus points are possible for a valuable clarification to a completely correct answer.
• Test 2
This is a closed-book test (the use of any materials is prohibited). The answers on each task have a fixed cost in points. The cost of a specific question corresponds to its complexity and the estimated time and effort that the student must spend to answer it. On open questions, bonus points are possible for a valuable clarification to a completely correct answer.
• Project

Interim Assessment

• 2021/2022 4th module
0.4 * Project + 0.25 * Test 2 + 0.25 * Test 1 + 0.1 * Home assignments

Recommended Core Bibliography

• Introduction to econometrics, Stock, J. H., 2003
• 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