MACHINE LEARNING FOR QUANTITATIVE ECONOMICS
Academic year and teacher
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- Versione italiana
- Academic year
- 2022/2023
- Teacher
- ANTONIO MUSOLESI
- Credits
- 7
- Curriculum
- Green economy and sustainability
- Didactic period
- Secondo Semestre
- SSD
- SECS-P/05
Training objectives
- 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. Prerequisites
- Probability and statistical inference; basic linear algebra; basic econometrics
Course programme
- 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 Didactic methods
- Lectures and lab exercises
Learning assessment procedures
- Oral examination
Reference texts
- 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
