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Course Syllabus

Master 2021/2022

Statistical Analysis and Statistical Packages

Category 'Best Course for Broadening Horizons and Diversity of Knowledge and Skills'
Type: Compulsory course (Population and Development)
Area of studies: Public Administration
Delivered by: Department of Applied Economics
Where: Faculty of Social Sciences
When: 1 year, 1-4 module
Mode of studies: offline
Open to: students of one campus
Instructors: Vladimir A. Kozlov, Dmitry Malakhov, Daniil Romanov
Master’s programme: Population and Development
Language: English
ECTS credits: 6
Contact hours: 90

Course Syllabus

Full Syllabus

Abstract

This course is a gentle introduction to modern applied statistics and econometrics. The course is based on the following principle: first, idea and formal description of mathematical concepts are given, second, these concepts are applied to real-world problems. The course has three main chapters: probability theory, statistics, and econometrics. Programming in R will be a red thread through all topics. Usage of R helps to apply statistical techniques to real data. The probability theory’s part is devoted to the most fundamental aspects of statistical analysis. Moreover, during this part we will also cover R programming, therefore, the first part of the course will form foundations for further topics. The statistics’ part explains principles of the basic applied statistical analysis and serves as a bridge between probability theory and the most applied part of the course, econometrics. Econometrics is a collection of mathematical tools which helps to forecast variables, find new dependences and test theories.
Learning Objectives

Learning Objectives

  • The goal of this course is to refresh, broaden, and systematize students’ knowledge of statistics and econometrics and to practice its application. . The course is elementary and presents concepts and techniques in way that benefits students of all mathematical backgrounds. Fundamental concepts and methods of statistics and econometrics are introduced with emphasis on interpretation of arguments and application to real-world problems. Every topic will be backed up with an applied exercise.
Expected Learning Outcomes

Expected Learning Outcomes

  • 1. be able to present the basic concepts and methods of statistical reasoning and data analysis in the context of decision-making;
  • 2. develop computational skills in fundamental statistical analysis;
  • 3. acquire a basic/working knowledge of data analysis using R package (STATA is optional);
  • 4. demonstrate the appropriate level of competence regarding the fundamentals of statistics and econometrics;
  • 5. demonstrate the appropriate level of competence in written expression.
Course Contents

Course Contents

  • A.1 Recapitulation: Data description and numerical measures.
  • A.2 Recapitulation: Probabilities, probability distributions, sampling and estimation, and hypothesis testing.
  • B.1 OLS, the assumptions and the properties of OLS estimators.
  • B.2 Hypothesis Testing after OLS estimation.
  • B.3 Multiple linear restrictions, R-squared.
  • C.1 Nonlinear and discrete independent variables.
  • C.2 Departures from OLS assumptions.
  • C.3 Misspecifications.
  • C.4 Introduction to qualitative dependent variables I.
  • C.5 Introduction to qualitative.
  • C.6 Introduction to Time Series.
  • C.7 Endogeneity.
Assessment Elements

Assessment Elements

  • non-blocking Homework Assignments
    Late homework submission WILL NOT BE ACCEPTED. Only students are allowed to work on homework assignments individually.
  • non-blocking Project
    - Students should collect datasets by themselves, set a research question and analyse the data.
  • non-blocking Calculus Exam
    - Retake of this is allowed if the student want try to increase the grade. - ONLY BASIC CALCULATORS will be allowed in the exams. Closed-book form.
  • non-blocking Winter midterm exam
    - No retake is allowed. - ONLY BASIC CALCULATORS will be allowed in the exams. - Winter midterm is a closed-book exam. The exam will be held in a distant format
  • non-blocking Spring midterm
    - No retake is allowed. - ONLY BASIC CALCULATORS will be allowed in the exams. - Winter midterm is a closed-book exam.
  • non-blocking Final exam
    - No retake is allowed. - ONLY BASIC CALCULATORS will be allowed in the exams. - Winter midterm is a closed-book exam.
Interim Assessment

Interim Assessment

  • 2021/2022 4th module
    Final exam = 0.1*Calculus exam + 0.15*Home assignments + 0.15*Winter midterm + 0.1*Spring midterm +0.1*Project + 0.3*Final exam
Bibliography

Bibliography

Recommended Core Bibliography

  • Introductory econometrics : a modern approach, Wooldridge, J. M., 2009

Recommended Additional Bibliography

  • David Card. (n.d.). Wed Jun 27 22:35:26 2007THE IMPACT OF THE MARIEL BOATLIFT ON THE MIAMI LABOR MARKET. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.ED3A80FA
  • Steven Berry, James Levinsohn, & Ariel Pakes. (1995). Automobile prices in market equilibrium. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.6F437C07