Econometrics

Bachelor 2022/2023

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, Elena V. Semerikova

Language: English

ECTS credits: 7

Contact hours: 120

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

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

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 assignments Semester 1
October mid-term
The students sit two mid-term written exams in October and in March, first semester written exam in December, and HSE final exam. All exams include multiple choice and free response parts.
December exam
December exam and HSE final exam have a status of exam in the Curriculum with possible retakes, while October and March mid-term exams have a status of Control Paper (with no retakes).
March Mid-term
Final exam
Applied essay
Home assignments Semester 2

Interim Assessment

2022/2023 2nd module
0.5 * December exam + 0.25 * Home assignments Semester 1 + 0.25 * October mid-term
2022/2023 4th module
G=0.3*(0.25*Goct+0.5*Gdec+0.25*Gha1)+0.2*Gmarch+0.1*Gha2+Gessay+0.4Gfin+Gbonus In the second semester the applied essay is set with the bonus points out of 10. As a rule, the applied essay will be required to be presented and defended by the author.

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

Course Syllabus