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Regular version of the site
2023/2024

Statistical Analysis II

Type: Mago-Lego
When: 3, 4 module
Open to: students of one campus
Instructors: Ekaterina Aleksandrova, Daria Salnikova
Language: English
ECTS credits: 6
Contact hours: 48

Course 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: panel data analysis, causal inference and categorical data analysis. 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. 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 improve students’ skills in the linear regression analysis, to learn how to estimate the model with the binary dependent variable, to learn how to estimate FE and RE panel models, learn how to estimate difference-in-differences model, to make students familiar with the basic tools for testing theories, to make students able to read, interpret and replicate the results of published papers using standard computer packages and real-world data
Expected Learning Outcomes

Expected Learning Outcomes

  • Be able to use theoretical notions, concepts and interpret the models with Panel Data.
  • to learn how to estimate the model with the binary variable
  • Explain the difference between fixed-effect, random-effects, and first-difference models; the parallel trends assumption
  • Be able to address endogeneity problems
  • Know properties of maximum likelihood estimates.
  • be able to identify cases when it is possible to use IV regression models
  • be able to estimate the IV regression model
  • be able to define and use the maximum likelihood estimation approach
  • be able to apply difference-in-differences model
  • be able to interpret the difference-in-differences model
  • be able to identify a strong and a weak instrument
  • to be able to analyze and estimate Panel Data models on real data
Course Contents

Course Contents

  • Endogeneity. Instrumental variables method. 2SLS
  • Binary dependent variables. Logit and probit models
  • Maximum Likelihood Estimation
  • Panel Data Models
  • Difference-in-Differences
  • Regression models with interaction terms
Assessment Elements

Assessment Elements

  • non-blocking Test 1
  • non-blocking Test 2:
    -
  • non-blocking Exam
  • non-blocking Home assignment 1
  • non-blocking Home assignment 2
Interim Assessment

Interim Assessment

  • 2023/2024 4th module
    0.4 * Exam + 0.1 * Home assignment 1 + 0.1 * Home assignment 2 + 0.2 * Test 1 + 0.2 * Test 2:
Bibliography

Bibliography

Recommended Core Bibliography

  • Data analysis using regression and multilevel/hierarchical models, Gelman, A., 2009

Recommended Additional Bibliography

  • Beck, V. L. (2017). Linear Regression : Models, Analysis, and Applications. Hauppauge, New York: Nova Science Publishers, Inc. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1562876