10532 - Applied Econometrics

Learning outcomes

The student will learn the fundamentals of the classical linear multiple regression model.

In particular, the student:  

• will be able to critically understand and discuss the empirical applications of the regression methods;
• will be able to perform empirical analysis using the software STATA.

Course contents

• What is econometrics? Introduction to the simple linear regression model.
• Estimating the coefficients of the linear regression model: the Ordinary Least Squares Estimator.
• Goodness-of-fit.
• Hypothesis testing and confidence intervals.
• Omitted variable bias.
• The multiple linear regression model.
• Binary regressors.
• Non linear regression models.
• Advanced topics: panel data and instrumental variables.
• Empirical examples: interpretation and discussion of empirical results.

Readings/Bibliography

Stock, J.H., Watson, M.W. "Introduzione all'econometria", 4a edizione (2016), Pearson Education Italia.

Teaching methods

Traditional lecture-style instruction with debate over real-world practice examples. For every theoretical topic an empirical application is illustrated. 

Computer-based review sessions complete the teaching by providing useful tools that allow students to interpret and discuss empirical results.

Assessment methods

Student evaluation is based on:

• problem set on STATA (at most 4 students);
  
• written exam.

Teaching tools

• Slides available at: https://sites.google.com/site/marcobmagnani;
• Stata software,  available on computers at the lab, Department of Economics.

Office hours

See the website of Marco Magnani