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Course Syllabus

Bachelor 2022/2023

Econometrics

Type: Compulsory course (International Programme in Economics and Finance)
Area of studies: Economics
Delivered by: International College of Economics and Finance
Where: International College of Economics and Finance
When: 3 year, 1-4 module
Mode of studies: distance learning
Online hours: 36
Open to: students of one campus
Instructors: Каныгина Надежда Геннадьевна, Нартикоев Алан Ревазович, Albert Belkov, Vladimir Ilyich Chernyak, Elena V. Semerikova, Oleg O. Zamkov, Кузьмин Григорий Иванович
Language: English
ECTS credits: 9
Contact hours: 120

Course Syllabus

Full Syllabus

Abstract

Course Pre-requisites. Statistics, Mathematics for Economists, Introduction to Economics. Course description: The Elements of Econometrics is an introductory full year course for the 3-rd year ICEF students. The course is taught in English. 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 actual Economic data, in particular recent data for Russian economy, is used for interpretations and applications. 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
  • 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
  • Collect and adjust the real economic data for the application of Econometrics methods and models;
Expected Learning Outcomes

Expected Learning Outcomes

  • Analyze and estimate SLR model on real economic data using econometric software
  • Analyze and estimate MLR model on real economic data using econometric software
  • Analyze and estimate LRM model in various specifications with real economic data using econometric software
  • Analyse reasons, consequences, methods of detection and remedial measures for heteroscedasticity
  • Analyze and estimate Binary Choice Models and Limited Dependent Variable Models on real economic data using econometric software
  • Analyze and estimate Dynamic Processes models on real economic data using econometric software
  • Analyse reasons, consequences, methods of detection and remedial measures for the models with Autocorrelated Disturbance Term
  • Analyze and estimate the models with Autocorrelated Disturbance Term on real economic data using econometric software
  • Analyze and estimate Panel Data models on real economic data using econometric softwar
  • Analyze and estimate models with dummy variables 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
  • Linear Regression Model Specification
  • Heteroscedasticity
  • Stochastic Regressors. Measurement Errors. Instrumental Variables.
  • Simultaneous Equations Models
  • Binary Choice Models, Limited Dependent Variable Models
  • Maximum Likelihood Estimation
  • Modelling with Time Series Data. Dynamic Processes Models
  • Autocorrelated disturbance term
  • Time Series Econometrics: Nonstationary Time Series
  • Panel Data Models
  • Dummy Variables
Assessment Elements

Assessment Elements

  • non-blocking home assignments in semester 1
  • non-blocking October midterm
  • non-blocking home assignments in semester 2
  • non-blocking March midterm
  • non-blocking December exam
  • non-blocking Final exam
Interim Assessment

Interim Assessment

  • 2022/2023 2nd module
    0.5 * December exam + 0.25 * October midterm + 0.25 * home assignments in semester 1
  • 2022/2023 4th module
    0.3 * 2022/2023 2nd module + 0.1 * home assignments in semester 2 + 0.2 * March midterm + 0.4 * Final exam
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

Recommended Additional Bibliography

  • 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

Presentation

  • Course Syllabus

Authors

  • ZAMKOV OLEG OLEGOVICH