2026/2027
Статистический анализ. Продвинутый уровень
Статус:
Маго-лего
Кто читает:
Кафедра высшей математики
Где читается:
Общеуниверситетские кафедры
Когда читается:
4 модуль
Охват аудитории:
для своего кампуса
Преподаватели:
Сальникова Дарья Вячеславовна
Язык:
английский
Кредиты:
3
Контактные часы:
32
Course Syllabus
Abstract
This course offers a practical introduction to modern applied statistics and econometrics, assuming that participants already have a basic knowledge of statistical inference, and linear regression models. The teaching approach follows a clear two‑step logic: each mathematical concept is first introduced intuitively and then formalized, after which it is immediately put into practice on real‑world problems.
The curriculum is built around three core modules: 1) regression models with interaction terms
2) logistic regression models for binary outcomes
and 3) an introduction to panel data analysis (covering basic specifications and estimation techniques)
Throughout the course, Python is used as the primary computational tool, enabling students to apply statistical methods to authentic datasets, visualize results, and interpret outputs.
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 Python and real-world data
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
- Know properties of maximum likelihood estimates.
- 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
- to be able to analyze and estimate Panel Data models on real data
- Able to run regression models with interaction terms
Course Contents
- Regression models with interaction terms
- Panel Data Models
- Difference-in-Differences
- Binary dependent variables. Logit and probit models
Interim Assessment
- 2026/2027 4th module0.1 * Seminar Activity + 0.1 * Home assignment 1 + 0.15 * Test 1 + 0.15 * Test 2: + 0.1 * Home assignment 2 + 0.4 * Exam
Bibliography
Recommended Core Bibliography
- Counterfactuals and causal inference : methods and principles for social research, Morgan, S. L., 2015
- Data analysis using regression and multilevel/hierarchical models, Gelman, A., 2009
- Introduction to econometrics, Stock, J. H., 2008
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