MACHINE LEARNING FOR QUANTITATIVE ECONOMICS
Anno accademico e docente
Non hai trovato la Scheda dell'insegnamento riferita a un anno accademico precedente?
Ecco come fare >>
- English course description
- Anno accademico
- 2022/2023
- Docente
- ANTONIO MUSOLESI
- Crediti formativi
- 7
- Percorso
- Green economy and sustainability
- Periodo didattico
- Secondo Semestre
- SSD
- SECS-P/05
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
Contenuti del corso
- 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 Metodi didattici
- Lectures and lab exercises
Modalità di verifica dell'apprendimento
- Oral examination
Testi di riferimento
- 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
