Бакалавриат
2025/2026
Эконометрика
Статус:
Курс обязательный (Международная программа по экономике и финансам)
Направление:
38.03.01. Экономика
Кто читает:
Международный институт экономики и финансов
Где читается:
Международный институт экономики и финансов
Когда читается:
3-й курс, 1-4 модуль
Формат изучения:
без онлайн-курса
Охват аудитории:
для своего кампуса
Язык:
английский
Кредиты:
8
Course 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
- 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;
- Collect and adjust the real economic data for the application of Econometrics methods and models
- Use the AI assistance for econometric analysis
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
- Introduction to Econometrics
- Simple Linear Regression Model (SLR) with Non-stochastic Explanatory Variables. OLS estimation
- Multiple Linear Regression Model (MLR): Estimation and Inference
- Linear Regression Model Specification
- Multiple Regression Analysis: Variables Transformations, Non-Linear Models.
- Multiple Regressions with Qualitative Information. Dummy Variables.
- Heteroscedasticity
- Stochastic Regressors. Measurement Errors. Instrumental Variables.
- Simultaneous Equations Models
- 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
- October midterm
- Final examIn order to get a passing grade for the course, the student must sit (all parts) of the examination.
- January midterm
- March midterm
- home assignments
Interim Assessment
- 2025/2026 4th module0.5 * Final exam + 0.15 * January midterm + 0.15 * March midterm + 0.1 * October midterm + 0.1 * home assignments
