Discrete Choice Models and Consumer Behavior

2021/2022

Academic Year

ENG

Instruction in English

5

ECTS credits

Delivered at:

Department of Economics

Course type:

Elective course

When:

4 year, 1 module

Instructors

Ozhegov, Evgeniy M.

Pokrovsky, Dmitry A.

Susin, Ivan

Course Syllabus

Abstract

The purpose of the course "Discrete Choice Models and Consumer Behavior" is to give students the skills to use econometrics in practice, to show the relationship between econometric theory and microeconomic theory of consumer behavior with everyday economic problems of demand estimation, to give a fundamental idea of applied empirical industrial organization as a branch of economic science.

Learning Objectives

• Apply their knowledge and understanding of the science of behavior to real world situations
• Demonstrate a wide range of generic skills, including skills in communication, information processing, teamwork, critical and creative thinking, computing independent learning

Expected Learning Outcomes

• Know basic theoretical models of consumer choice and utility maximization
• Link between type of preferences and utility function
• Specify an econometric model of demand to fit the data and consumer preferences
• Use statistical packages to calibrate parameters of demand model Interpret parameters of calibrated model

Course Contents

• Marshallian demand
• Demand systems

Assessment Elements

• Exam
• In-class Participation
• written assignments 2
• written assignments 1

Interim Assessment

• 2021/2022 1st module
  0.4 * Exam + 0.2 * In-class Participation + 0.2 * written assignments 1 + 0.2 * written assignments 2

Bibliography

Recommended Core Bibliography

• Jean-Pierre H. Dubé. (2018). Microeconometric Models of Consumer Demand. NBER Working Papers. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.p.nbr.nberwo.25215

Recommended Additional Bibliography

• Patrick Bajari, Denis Nekipelov, Stephen P. Ryan, & Miaoyu Yang. (2015). Demand Estimation with Machine Learning and Model Combination. NBER Working Papers. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.p.nbr.nberwo.20955

Authors

• OZHEGOV EVGENIY MAKSIMOVICH