2024/2025
Статистический анализ. Продвинутый уровень
Статус:
Маго-лего
Кто читает:
Кафедра высшей математики
Когда читается:
3, 4 модуль
Охват аудитории:
для своего кампуса
Преподаватели:
Сальникова Дарья Вячеславовна
Язык:
английский
Кредиты:
6
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. Usage of Python helps to apply statistical techniques to real data. Econometrics is a collection of mathematical tools which helps to forecast variables, find new dependences and test theories.
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
- 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
- Able to run regression models with interaction terms
Course Contents
- Regression models with interaction terms
- Panel Data Models
- Difference-in-Differences
- Endogeneity. Instrumental variables method. 2SLS
- Maximum Likelihood Estimation
- Binary dependent variables. Logit and probit models
Assessment Elements
- Test 1
- Home assignment 1
- Test 2:-
- Home assignment 2
- Exam
- Seminar Activity
- Home Assignment 3
Interim Assessment
- 2024/2025 4th module0.45 * Exam + 0.05 * Home Assignment 3 + 0.05 * Home assignment 1 + 0.05 * Home assignment 2 + 0.1 * Seminar Activity + 0.15 * Test 1 + 0.15 * Test 2:
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