The goal of the course is to give students a practical and applicable introduction to all key econometrics concepts of modern statistical approaches and data science practice. During the course, students will conduct both theoretical reviews and practical exercises in econometrics. Course will include high level reviews of all key econometrics approaches and exercises. Students must know the basics of calculus, probability theory and bring a sense of humor 


Part 1 Introduction to Econometrics

● Introductions

● Descriptive statistics

● Regression Analysis with Cross-Sectional Data

● The Simple Regression Model

● Multiple Regression Analysis: Estimation

● Multiple Regression Analysis: Inference

● Multiple Regression Analysis: OLS Asymptotics

● Multiple Regression Analysis: Further Issues

● Multiple Regression Analysis with Qualitative Information: Binary (or Dummy) Variables

● Heteroskedasticity

● Specification and Data Issues

Part 2 Time series

● Regression Analysis with Time Series Data

● Basic Regression Analysis with Time Series Data

● Issues in Using OLS with Time Series Data

● Serial Correlation and Heteroskedasticity in Time Series Regressions

Part 3 Panel data

● Pooling Cross Sections Across Time: Simple Panel Data Methods

● Advanced Panel Data Methods

● Instrumental Variables Estimation and Two Stage Least Squares

● Simultaneous Equations Models

● Limited Dependent Variable Models and Sample Selection Corrections

Part 4 Modern approaches

● Non parametric methods in econometrics

● Unsupervised methods in econometrics

● Social Network Analysis application


Наталія Терлич

Ключові факти:

Навчальний семестр: 4  

Кількість кредитів: 6 ECTS

Освітня програма:  ІТ та бізнес-аналітика