77216 - MACHINE LEARNING FOR QUANTITATIVE ECONOMICS

insegnamento

ID:

77216

Tipo Insegnamento:

Opzionale

Durata (ore):

56

CFU:

SSD:

ECONOMETRIA

Url:

Dettaglio Insegnamento:

ECONOMICS, MANAGEMENT AND POLICIES FOR GLOBAL CHALLENGES/Green economy and sustainability Anno: 2

Dettaglio Insegnamento:

ECONOMICS, MANAGEMENT AND POLICIES FOR GLOBAL CHALLENGES/Small and medium enterprises(SMEs) in international markets Anno: 2

Anno:

2024

Periodo di attività

Primo Semestre (16/09/2024 - 20/12/2024)

Obiettivi Formativi

The course aims at ii) presenting some intermediate econometric methods (Instrumental variables, panel data, and time series models), ii) discussing the use of statistical learning methods to make causal analysis, and iii) providing computational tools using R.

At the end of the course, the student is able to use some intermediate econometric and machine learning methods in order to study causal relations and to apply them using R.

Prerequisiti

Probability and statistical inference; basic linear algebra; basic econometrics

Metodi didattici

Lectures and lab exercises

Verifica Apprendimento

Oral examination

Testi

Wooldridge J., Introductory Econometrics: A Modern Approach

James, G., Witten, D., Hastie, T., & Tibshirani, R., An introduction to statistical learning with Applications in R

Kleiber, C., & Zeileis, A., Applied econometrics with R

Contenuti

Cross-sectional regression:
- review of basic notions
- dealing with endogeneous regressors
-using the (linear) regression model for causal inference when the explanatory variable is discrete

Time-series regression:
basic notions

Panel-data:
- unobserved heterogeneity and endogeneity
- models with heterogeneous slopes
- dealing with cross-sectional dependence

Machine learning for causal analysis
- resampling methods
- model selection and regularization
- moving beyond linearity: parametric and nonparametric regression