Магистратура
2024/2025
Статистический анализ. Начальный уровень.
Статус:
Курс обязательный (Население и развитие / Population and development)
Направление:
38.04.04. Государственное и муниципальное управление
Кто читает:
Кафедра высшей математики
Где читается:
Факультет социальных наук
Когда читается:
1-й курс, 1, 2 модуль
Формат изучения:
без онлайн-курса
Охват аудитории:
для своего кампуса
Преподаватели:
Сальникова Дарья Вячеславовна
Прогр. обучения:
Население и развитие
Язык:
английский
Кредиты:
6
Контактные часы:
64
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: probability theory, statistics, and econometrics. 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
- The aim of the course is to acquaint students with the main concepts and methods of applied statistical analysis and econometrics
Expected Learning Outcomes
- Able to identify overfitting
- Be able to use the methods of descriptive statistics to summarize and visualize the raw data.
- to be aware of the consequences of the omitted variable bias
- makes a statistical inference by the significance level and by p-value
- Be able to compute and interpret coefficient of determination.
- Explain the relationship between confidence interval estimates and p-values in drawing inferences
- Apply linear regression models in practice: identify situation where linear regression is appropriate; build and fit linear regression models with software; interpret estimates and diagnostic statistics; produce exploratory graphs
- Understand potential outcome and directed acyclic graph approaches
- to be aware of the difference between the theoretical and empirical estimand
- to be able to explain the essence of the selection bias problem and be aware of the consequences of this problem
- to be able to construct and interpret confidence intervals
- to be able to test the equality of variances
- to be able to test the equality of means
- to be able to explain the essence of the post-treatment bias
- to be able to detect and remedy multicollinearity
- to be able to detect and remedy heteroskedasticity
Course Contents
- Introduction: Bridging the Gap between Theory and Empirical Research
- Statistical Inference: Parameter Estimation
- Statistical Inference: Hypothesis Testing
- Exploratory Data Analysis
- Specifying the Relationship between Variables. Directed Acyclic Graphs
- Linear Regression Models: Model Specification, Interpretation and Hypothesis Testing
- Assessing Goodness-of-Fit in Linear Regression Models
- Diagnostics in Linear Regression Models
Assessment Elements
- Seminar Activity
- Home Assignment 1
- Home Assignment 2
- Home Assignment 3
- Test 1
- Test 2
- Exam
Interim Assessment
- 2024/2025 2nd module0.3 * Exam + 0.1 * Home Assignment 1 + 0.1 * Home Assignment 2 + 0.1 * Home Assignment 3 + 0.1 * Seminar Activity + 0.15 * Test 1 + 0.15 * Test 2
Bibliography
Recommended Core Bibliography
- Basic econometrics, Gujarati, D. N., 2009
- Jeffrey M. Wooldridge. (2019). Introductory Econometrics: A Modern Approach, Edition 7. Cengage Learning.
- Mastering 'Metrics : the path from cause to effect, Angrist, J. D., 2015
- Mathematical statistics with applications, Wackerly, D. D., 2008
- Morgan, S. L., & Winship, C. (2007). Counterfactuals and Causal Inference : Methods and Principles for Social Research. New York: Cambridge University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=206937
Recommended Additional Bibliography
- Core concepts in data analysis: summarization, correlation and visualization, Mirkin, B., 2011
- Core data analysis : summarization, correlation, and visualization, Mirkin, B., 2019
- Mostly harmless econometrics : an empiricist's companion, Angrist, J. D., 2009