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Bachelor 2021/2022

# Elements of Econometrics

Type: Compulsory course
Area of studies: Economics
When: 3 year, 1-4 module
Mode of studies: distance learning
Open to: students of one campus
Language: English
ECTS credits: 10

### Course Syllabus

#### Abstract

The Elements of Econometrics is an introductory full year course for the 3-rd year ICEF students. The course is taught in English and finally examined by the University of London international programme, or by ICEF 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.

#### Learning Objectives

• - apply econometric methods to the investigation of economic relationships and processes;
• The students get in the course 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.
• - verify economic facts, theories and models with real data;
• The students should understand essential differences between the time series and cross section data and those specific econometric problems met in the work with these types of data (measurement errors, endogeneity, autocorrelation, non-stationarity and others), as well as with panel data, and apply the appropriate econometric methods (instrumental variables, maximum likelihood estimation, models of dynamic processes, etc.). The students should get the skills of construction and development of linear regression models, get acquainted with some non-linear models and special methods of econometric analysis and estimation (binary choice models, , understanding the area of their application in economics. The methods and models should be mastered practically on real economic data sets with modern econometric software.
• - evaluate the quality of statistical and econometric analysis;
• The course contributes in the development of the following general competencies: -ability to work with information: to find, evaluate and use information from various sources, necessary to solve scientific and professional problems; - ability to do research, including problem analysis, setting goals and objectives, identifying the object and subject of research, choosing the means and methods of research, assessing its quality; - ability to collect and analyze the data and calculate economic and socio-economic indicators characterizing the activities of economic entities; - ability to choose tools for processing economic data, analyze the results of calculations and substantiate the findings; - ability to analyze and interpret the data of domestic and foreign statistics on socio-economic processes and phenomena, to identify trends in changing of socio-economic indicators; - ability to solve analytical and research problems with modern technical means and information technologies; - ability to organize their activities in the framework of professional tasks.
• - 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

#### Expected Learning Outcomes

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

#### 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 Explanatory Variables. 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

• December exam
Online format.
• October midterm
• home assignments in semester 1
• March midterm
• home assignments in semester 2
• class participation, contests, applied essay
Additional bonus points can be given by the lecturer for class participation during the year (not more than 5 out of 100), and for the Universiade and other contests. In the second semester the applied essay is set with the bonus points given up to 10 out of 100 extra to the year grade.
• the University of London (or ICEF final) exam

#### Interim Assessment

• 2021/2022 1st module
• 2021/2022 2nd module
0.5 * December exam + 0.25 * October midterm + 0.25 * home assignments in semester 1
• 2021/2022 3rd module
• 2021/2022 4th module
0.3 * 2021/2022 4th module + 0.2 * March midterm + 0.1 * home assignments in semester 2 + 0.4 * the University of London (or ICEF final) exam