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Course Syllabus

Bachelor 2020/2021

Econometrics

Type: Compulsory course (HSE University and University of London Parallel Degree Programme in Management and Digital Innovation)
Area of studies: Business Informatics
Delivered by: Bachelor's Programme in Digital Product Management
Where: Graduate School of Business
When: 3 year, 1, 2 module
Mode of studies: distance learning
Instructors: Margarita Gladkova, Dmitry Sizykh
Language: English
ECTS credits: 4
Contact hours: 40

Course Syllabus

Full Syllabus

Abstract

The course is aimed to provide students with necessary knowledge of quantitative tools and techniques which allow to conduct the analysis of data in order to make better business decisions. By the end of the course students will be able to build and estimate the quantitative models to test economic theories. The stress in the course is done on the essence of statements, methods and approaches of econometric analysis. The conclusions and proofs of basic formulas and models are given which allows the students to understand the principles of econometric theory development. The main attention is paid to the economic interpretations and applications of the econometric models. The first part of the course is devoted to the cross-section econometrics; the second part – to the time series and panel data econometrics.
Learning Objectives

Learning Objectives

  • Apply econometric methods to the investigation of economic relationships and processes;
  • Verify economic facts, theories and models with real data;
  • Evaluate the quality of statistical and econometric analysis;
  • Carry out and evaluate forecasting for time series and cross section data;
  • Understand econometric methods, approaches, ideas, results and conclusions met in economic books and articles.
Expected Learning Outcomes

Expected Learning Outcomes

  • Outline the subject of Econometrics, its approach, the sources for study materials (including online ones), data, software, the course outcomes
  • Be able to explain the need for variables transformations in Econometric analysis
  • Be able to analyze and estimate models with dummy variables on real economic data using statistical software package “Stata”.
  • Be able to transform and estimate econometrics models with heteroscedasticity on real economic data.
  • Be able to create and apply multiple regression models.
  • Be able to apply the Binary Choice Models and Limited Dependent Variable Models
  • Be able to analyze and estimate Binary Choice Models and Limited Dependent Variable Models on real economic data using econometric software
  • Be able to apply Autocorrelation test: Durbin-Watson Test and Breusch-Godfrey Test.
  • Be able to use theoretical notions, concepts and interpret results of modeling with Time Series Data using statistical software package “Stata”.
  • Be able to use theoretical notions, concepts and interpret the models with Panel Data.
  • Be able to analyze and estimate Panel Data models on real economic data using statistical software package “Stata”.
Course Contents

Course Contents

  • Data collection and description. Hypothesis testing.
  • Methodology of econometrics. Pair-wise regression.
  • Multiple regression. Omitted and redundant variables. Specification tests. Multicollinearity. Heteroscedasticity.
  • Binary choice models. Probit regression model. Logit regression model.
  • Autocorrelation. Durbin-Watson Test. Breusch-Godfrey Test.
  • Time series models and forecasting. ARIMA models.
  • Panel data regression.
Assessment Elements

Assessment Elements

  • non-blocking Mini-tests
  • non-blocking Practical works
  • non-blocking Exam
    During the exam for calculations is allowed to use next programs: Stata, MS Excel. During the exam is allowed to use draft and pen for solving of tasks.
Interim Assessment

Interim Assessment

  • Interim assessment (2 module)
    0.5 * Exam + 0.1 * Mini-tests + 0.4 * Practical works
Bibliography

Bibliography

Recommended Core Bibliography

  • Dougherty, C. (2016). Introduction to Econometrics. Oxford University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.b.oxp.obooks.9780199676828
  • Dubrocard, A., & Allegrezza, S. (2012). Internet Econometrics. Basingstoke: Palgrave Macmillan. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=460814
  • Enders, W. (2015). Applied Econometric Time Series (Vol. Fourth edition). Hoboken, NJ: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1639192
  • Jeffrey M Wooldridge. (2010). Econometric Analysis of Cross Section and Panel Data. The MIT Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.b.mtp.titles.0262232588

Recommended Additional Bibliography

  • Wooldridge, J. M. . (DE-588)131680463, (DE-576)298669293. (2006). Introductory econometrics : a modern approach / Jeffrey M. Wooldridge. Mason, Ohio [u.a.]: Thomson/South-Western. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edswao&AN=edswao.250894459