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Course Syllabus

Bachelor 2023/2024

Econometrics

Type: Compulsory course (Data Science and Business Analytics)
Area of studies: Applied Mathematics and Information Science
Delivered by: Big Data and Information Retrieval School
Where: Faculty of Computer Science
When: 3 year, 1-4 module
Mode of studies: offline
Open to: students of one campus
Instructors: Ksenia Kasianova
Language: English
ECTS credits: 7
Contact hours: 112

Course Syllabus

Full Syllabus

Abstract

Econometrics is an introductory full year course for the 3-rd year students of the DSBA programme of NRU HSE. The course is taught in English and finally examined by HSE final exam. 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. Prerequisites: Statistics, Mathematical Methods or Mathematics for Economists, Introduction to Economics
Learning Objectives

Learning Objectives

  • Basic knowledge and skills of econometric analysis and its application in economics. They should be able to: 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; do and evaluate forecasting for time series and cross section data; understand econometric methods, approaches, ideas, results and conclusions met in economic books and articles.
  • Understanding essential differences between the time series, cross section and panel data and those specific econometric problems met in the work with these types of data (measurement errors, endogeneity, autocorrelation, non-stationarity and others) and apply the appropriate econometric methods (instrumental variables, maximum likelihood estimation, models of dynamic processes, etc.).
  • Skills of construction and development of linear regression models, some non-linear models and special methods of econometric analysis and estimation (binary choice models, non-linear least squares, maximum likelihood estimation), understanding the area of their application in economics.
  • Methods and models should be mastered practically on real economic data sets with modern econometric software.
Expected Learning Outcomes

Expected Learning Outcomes

  • 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 the Binary Choice Models and Limited Dependent Variable Models
  • Be able to transform and estimate econometrics models with heteroscedasticity on real economic data.
  • Be able to use theoretical notions, concepts and interpret the models with Panel Data.
  • Outline the subject of Econometrics, its approach, the sources for study materials (including online ones), data, software, the course outcomes
  • be able to use theoretical notions, concepts and interpret results using SLR model
  • to analyze and estimate SLR model on real economic data using econometric software
  • be able to use theoretical notions, concepts and interpret using MLR model
  • to analyze and estimate MLR model on real economic data using econometric software
  • be able to explain the need for variables transformations in Econometric analysis
  • be able to use theoretical notions, concepts and interpret results related to dummy variables
  • to analyze and estimate models with dummy variables on real economic data using econometric software
  • be able to choose and interpret the LRM model specification
  • to analyze and estimate LRM model in various specifications with real economic data using econometric software
  • be able to analyse reasons, consequences, methods of detection and remedial measures for heteroscedasticity
  • be able to use theoretical notions, concepts and interpret results on the topic
  • be able to use, interpret and transform the Simultaneous Equations models and the concept of identification
  • be able to define and use the maximum likelihood estimation approach
  • be able to use theoretical notions, concepts and interpret results of modelling with Time Series Data
  • to analyze and estimate Dynamic Processes models on real economic data using econometric software
  • be able to analyse reasons, consequences, methods of detection and remedial measures for the models with Autocorrelated Disturbance Term
  • to analyze and estimate the models with Autocorrelated Disturbance Term on real economic data using econometric software
  • be able to use theoretical notions, concepts and interpret results on the models with Stationary and Nonstationary Time Series
  • to analyze and estimate the models with Stationary and Nonstationary Time Series on real economic data using econometric software
  • to analyze and estimate Panel Data models on real economic data using econometric software
Course Contents

Course Contents

  • Introduction to Econometrics
  • Simple Linear Regression Model (SLR) with Non-stochastic Explanatory Variables. OLS estimation
  • Multiple Linear Regression Model (MLR): two explanatory variables and k explanatory variables
  • Variables Transformations in Regression Analysis
  • Dummy Variables
  • Linear Regression Model Specification
  • Heteroscedasticity
  • Stochastic Explanatory Variables. Measurement Errors. Instrumental Variables
  • Simultaneous Equations Models
  • Maximum Likelihood Estimation
  • Binary Choice Models, Limited Dependent Variable Models
  • Modelling with Time Series Data. Dynamic Processes Models
  • Autocorrelated disturbance term
  • Time Series Econometrics: Nonstationary Time Series
  • Panel Data Models
Assessment Elements

Assessment Elements

  • non-blocking Exam_1
  • non-blocking HW_1
  • non-blocking Quizzes at seminars in semester 1
  • non-blocking Midterm_1
  • non-blocking Project_1
  • non-blocking HW_2
  • non-blocking Quizzes at seminars in semester 2
  • non-blocking Midterm_2
  • non-blocking Project_2
  • non-blocking Exam_2
  • non-blocking Bonus
Interim Assessment

Interim Assessment

  • 2023/2024 2nd module
    Grade 2nd module = 0.1*HW_1 + 0.2*Q_1 + 0.25*Midterm_1 + 0.15*Project_1 + 0.3*Exam_1
  • 2023/2024 4th module
    Grade 4th module = 0.1*HW_2 + 0.2*Q_2 + 0.25*Midterm_2 + 0.15*Project_2 + 0.3*Exam_2
Bibliography

Bibliography

Recommended Core Bibliography

  • Gujarati, D. (2014). Econometrics by Example (Vol. 2nd ed). Basingstoke: Palgrave Macmillan. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1525312
  • Introduction to econometrics, Dougherty, C., 2007
  • Introduction to econometrics, Dougherty, C., 2011
  • Introduction to econometrics, Dougherty, C., 2016
  • 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

  • Basic econometrics, Gujarati, D., 2009
  • Econometric analysis, Greene, W. H., 2000

Authors

  • Стоякина Елена Игоревна
  • Stankevich Ivan Pavlovich
  • SEMERIKOVA ELENA VYACHESLAVOVNA
  • Галевская Софья Андреевна
  • Бессмертный Александр Игоревич