Delivered at:: Department of Higher Mathematics (Independent HSE Departments)

Course type:: Compulsory course

When:: 1 year, 1, 2 module

Instructors

Kozlov, Nikita

Salnikova, Daria

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. 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 module
0.3 * 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

Authors

Буваева Роксана Викторовна
SALNIKOVA DARYA VYACHESLAVOVNA

Master’s Programme 'Population and Development'

Statistical Analysis I