DATA ANALYSIS
Instructional goals
The goal of this course is to provide students with statistic and econometric models and programming tools to handle data analysis.
Intended learning outcomes
Students will be familiar with basic econometric models, and develop skills to apply those models to actual business cases. They will be also trained to statistical programming with Python.
Course Contents
Main topics will discuss: simple and multiple regression, hypothesis testing, panel data, instrumental variables, and big data.
Reference Books
"Introduction to Econometrics-4th Global Edition" Stock, J and M. Watson, Pearson
Teaching Methods
Frontal lectures, theoretical exercises, and practical sessions with Python.
Assessment Method
Midterm test (30%, written) +
Group project (40%, oral presentation) +
Final exam (30%, written)
Thesis assignment criteria
Upon availability
Week 1
Review of Statistics and Probability
Python basis - a recap
Week 2
Simple Linear Regression
Week 3
Simple Linear Regression: Hypothesis Tests and Confidence Intervals
Week 4
Multiple Linear Regression
Week 5
Hypothesis Tests and Confidence Intervals in Multiple Regression
Week 6
Multiple Linear Regression
1. Regression with Dummy Variables
2. Regression with Categorical Variables
3. Regression
with Interactions among Variables
Week 7
Multiple Linear Regression
1. Regression with Dummy Variables
2. Regression with Categorical Variables
3. Regression
with Interactions among Variables
Week 8
Instrumental Variables Regression
Regression with a Binary Dependent Variable
Week 9
Regression with Panel Data
Week 10
Introduction to Time Series Regression and Forecasting
Week 11
Time Series Regression and Forecasting
Week 12
Project presentations