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Магистерская программа «Экономика и экономическая политика»

Models With Qualitative Dependent Variables

2022/2023
Учебный год
ENG
Обучение ведется на английском языке
3
Кредиты
Статус:
Курс по выбору
Когда читается:
1-й курс, 4 модуль

Преподаватель

Course Syllabus

Abstract

This course is devoted to binary choice models that are central in applied econometrics. We deal with the situation when the potential outcomes are discrete, i.e. the presence or absence of some quality of the object in question. It might also be the decision of an individual to perform or not to perform any action. The scope of application of these models is very wide. Classical examples are the problems of forecasting companies' defaults, employment equations, modeling the level of education, and many other problems of identifying the determinants of a certain choice and predicting its probability. In addition, we consider models with truncated dependent variable. The course includes Tobin and Heckman models that enables us to deal with truncated samples and selection bias. The course is applied in nature. Analysis of course’s topics is based on numerical examples. At the seminars, students use statistical software, i.e. STATA.
Learning Objectives

Learning Objectives

  • The main goal of the course is to explore methods of analyzing microeconomic data.
Expected Learning Outcomes

Expected Learning Outcomes

  • Students are able to estimate the models and interpret the results
Course Contents

Course Contents

  • Binary choice models
  • Multinomial models
  • Ordered choice models
  • Multivariate probit model
  • Truncation and censoring
Assessment Elements

Assessment Elements

  • non-blocking Individual hometask
  • non-blocking Exam
Interim Assessment

Interim Assessment

  • 2022/2023 4th module
    0.7 * Individual hometask + 0.3 * Exam
Bibliography

Bibliography

Recommended Core Bibliography

  • Econometric analysis of cross section and panel data, Wooldridge, J. M., 2010
  • Econometric analysis, Greene, W. H., 2012

Recommended Additional Bibliography

  • Applied logistic regression, Hosmer, D. W., 2000