Course Unit Page

Academic Year 2019/2020

Learning outcomes

At the end of the course student is familiarized with the main econometric tools used in the analysis on non linear models, or models for qualitative or latent variables, frequently used in empirical finance. More specifically, students will use maximum likelihood and GMM inference techniques, apply them to limited dependent variables, ARCH models, stochastic discount factor models. All applications will be conducted using the econometric software GRETL.

Course contents

1. Review of linear regression and ordinary least squares

2. Endogeneity and instrumental variables estimation

3. Linear panel data models

4. Discrete choice models

5. Maximum likelihood inference

6. Introduction to STATA


M. Verbeek, A guide to modern Econometrics, Wiley 2004.

Preliminary knowledge of basic econometrics necessary for this course can be acquired from:

Hill, Griffiths e Lim, Principles of Econometrics, 4th ed., Wiley, 2011, ch. 1-7, or M. Verbeek, A guide to modern Econometrics, Wiley 2004, ch. 1-4.

Teaching methods

For each topic we will first introduce the relevant theory, and then move as soon as possible to its empirical application using the software STATA. Special emphasis will be placed on the economic interpretation of the results.

Assessment methods

Written exam at the computer lab.

During the exam it is forbidden to consult notes, slides, books, pocket calculators and any other electronic devices. The purpose of the exam is to ascertain that students acquired the knowledge required to correctly specify, estimate and test the econometric models discussed during the lectures and possess the ability to properly interpret the results provided by these procedures.

Teaching tools


Links to further information


Office hours

See the website of Davide Raggi