96357 - BIG DATA APPLICATIONS

Conoscenze e abilità da conseguire

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.

Contenuti

Tools

- supervised learning

- unsupervised learning

- time series methods

Applications

- coincident indicators of economic activity

- forecasting of economic activity

- systemic risk

- assessment of monetary policies

Testi/Bibliografia

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

 

Metodi didattici

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.

 

Pre-requisites: Econometrics at the level of 2nd year EPOS course and Programming in R or Python

Modalità di verifica e valutazione dell'apprendimento

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

The students are required to give an oral presentation of their work. 

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.

Strumenti a supporto della didattica

Slides or handwritten notes on the whiteboard or on tablet.

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

Orario di ricevimento

Consulta il sito web di Matteo Barigozzi