DATA ANALYSIS
Obiettivi formativi
The goal of this course is to provide students with statistic and econometric models and programming tools to handle data analysis.
Risultati di apprendimento attesi
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.
Contenuti Del Corso
Main topics will discuss: simple and multiple regression, hypothesis testing, panel data, instrumental variables, and big data.
Testi Di Riferimento
"Introduction to Econometrics-4th Global Edition" Stock, J and M. Watson, Pearson
Metodologie Didattiche
Frontal lectures, theoretical exercises, and practical sessions with Python.
Modalità di verifica dell'apprendimento
Midterm test (30%, written) +
Group project (40%, oral presentation) +
Final exam (30%, written)
Criteri per l’assegnazione dell’elaborato finale
Upon availability
Settimana 1
Review of Statistics and Probability
Python basis - a recap
Settimana 2
Simple Linear Regression
Settimana 3
Simple Linear Regression: Hypothesis Tests and Confidence Intervals
Settimana 4
Multiple Linear Regression
Settimana 5
Hypothesis Tests and Confidence Intervals in Multiple Regression
Settimana 6
Multiple Linear Regression
1. Regression with Dummy Variables
2. Regression with Categorical Variables
3. Regression
with Interactions among Variables
Settimana 7
Multiple Linear Regression
1. Regression with Dummy Variables
2. Regression with Categorical Variables
3. Regression
with Interactions among Variables
Settimana 8
Instrumental Variables Regression
Regression with a Binary Dependent Variable
Settimana 9
Regression with Panel Data
Settimana 10
Introduction to Time Series Regression and Forecasting
Settimana 11
Time Series Regression and Forecasting
Settimana 12
Project presentations