96357 - BIG DATA APPLICATIONS

Learning outcomes

At the end of the course, students will be able to apply the main tools used in (supervised and unsupervised) machine learning to issues related to the field of economics, political science, business economics and law. Great emphasis will be given to applications related to financial markets.

Course contents

Tools

- supervised learning

- unsupervised learning

- time series methods

Applications

- coincident indicators of economic activity

- forecasting of economic activity

- systemic risk

- assessment of monetary policies

Readings/Bibliography

Introduction to Econometrics, J. Stock, M. Watson

An Introduction to Statistical Learning with Applications in R, G. James, D. Witten, T. Hastie, R. Tibshirani.

Lecture notes

Selected papers

 

Teaching methods

For each topic we will first introduce the relevant methods and then move to their application. Special emphasis will be placed on the economic interpretation of the results. Codes will be in Gretl, Matlab, or R.

Assessment methods

The exam consists in replicating a given paper assigned by the teacher and related to the topics covered during classes.

Alternatively, the student can propose a topic of interest whose consistency with the course contents must be evaluated and approved by the teacher.

In both cases the students are required to give an oral presentation of their work.

Depending on the chosen project students can work in groups.

The maximum possible score is 30 e lode. The exam is graded as follows:

<18 failed
18-23 sufficient
24-27 good
28-30 very good
30 e lode excellent

The final grade can be rejected only once.

Teaching tools

Slides or handwritten notes on the whiteboard or on tablet.

Codes to discuss empirical analysis and replicate the results of selected papers.

Office hours

See the website of Matteo Barigozzi