2025/2026
Эконометрика 1
Статус:
Майнор
Где читается:
Факультет компьютерных наук
Охват аудитории:
для всех кампусов НИУ ВШЭ
Преподаватели:
Слаболицкий Илья Сергеевич
Язык:
английский
Контактные часы:
56
Course Syllabus
Abstract
Econometrics studies quantitative and qualitative relationships between variables using probabilistic and statistical methods. Unlike machine learning, econometric modeling is primarily focused on identifying causal relationships and ensuring interpretability of results rather than on making predictions, which makes it possible to answer the questions about the direction and strength of the influence of one variables on others.
Learning Objectives
- to learn how econometric analysis works and apply it to the real business, economic and financial problems
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
- 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
- home assignment 1
- home assignment 2
- home assignment 3
- midterm
- linear regression challenge
- exam
Interim Assessment
- 2025/2026 2nd module0.25 * exam + 0.1 * home assignment 1 + 0.1 * home assignment 2 + 0.1 * home assignment 3 + 0.2 * linear regression challenge + 0.25 * midterm
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