Machine Learning Algorithms for Econometricians 

Bertinoro, 15 - 20 July 2019



Juri Marcucci
Bank of Italy
Via Nazionale 91, 00184 Rome, Italy




Basic Requirements

Intermediate knowledge of econometrics

Course outline

Algorithms, bagging, boosting, bootstrap, cross-validation, LASSO, misspecification, neural networks, nonlinearities, optimization, overfit, penalization, R, random forests, regression, splines, trees, etc.

Course description:

Do you feel lost in the random forests? Do you need some career boosting? Would you like to demystify magic words like cross-validation, bagging, shrinkage, etc? Or discover what is hidden behind wild acronyms like GAM, LASSO, GBM, etc. that you heard during that meeting or at the coffee machine or at that seminar with a fancy title? If so then you should consider attending this one-week intensive course on machine learning techniques.

These lectures has been conceived by econometricians for econometricians. The sessions proceed step by step, recalling the fundamental statistical concepts at the heart of the modern learning techniques. Their relative merits are illustrated by means of several case studies with real data.

The course will present Machine Learning Techniques to econometricians. In particular, the lecturers will

  • present various concepts intensively used in the Machine Learning literature such as cross-validation, bootstrap, optimization routines;
  • describe and explain popular machine learning techniques such as random trees, random forests, boosting, neural nets and deep learning, and their natural extensions to time series analysis and causal inference.


  • Ahamada, I. & E. Flachaire (2011). Non-Parametric Econometrics. Oxford University Press.
  • Berk, R.A. (2008). Statistical Learning from a Regression Perspective. Springer Verlag
  • Efron, B. & Hastie, T. (2016). Computer Age Statistical Inference: Algorithms, Evidence and Data Science. Cambridge University Press.
  • Hastie, T., Tibshirani, R. & Friedman, J. (2009). The Elements of Statistical Learning. Springer Verlag.
  • Hastie, T., Tibshirani, W. & Wainwright, M. (2015). Statistical Learning with Sparsity. Chapman CRC.
  • Watt, J., Borhani, R. & Katsaggelos, A. (2016). Machine Learning Refined : Foundations, Algorithms, and Applications. Cambridge University Press.

Course Schedule:

Monday July 15

Session 1A (Flachaire): Introduction, Model Misspecification, Nonlinearities, Nonparametric Econometrics (kernels, splines and GAMs)
Session 1B (Charpentier): Loss Functions, Objective Functions and Penalty (quantile regression, LASSO, ridge)

Tuesday July 16

Session 2A (Flachaire): Cross Validation, Overfit, Bootstrap and Bagging)
Session 2B (Flchaire): Classification, part I: logistic regression, trees, forests

Wednesday July 17

Session 3A (Charpentier): Classification, part II: neural networks and deep learning, and Model Selection (ROC, AUC))
Session 3B: excursion

Thursday July 18

Session 4A (Charpentier & Flachaire): Group “Hands-On Classification” on Real Data)
Session 4B (Flachaire): Regression: boosting, regression trees and forests

Friday July 19

Session 5A (Charpentier & Flachaire): Group “Hands-On Regression” on Real Data)
Session 5B (Charpentier): Algorithmic and Optimization Issues, Extension of Machine Learning Techniques to Time Series

Saturday July 20

Session 6A (Charpentier): Causality with Machine Learning Algorithms)


We will see along the lectures how to implement most of the techniques in R, with two “hands-on” sessions, one on classification problems and another one on regression, with real data. Participants are invited to bring their own laptop with R installed on it. Data sets and R code will be available through a supporting website.


Venue and timetables

The Module will last one week and will be held in the University Residential Centre, Via Frangipane 6, 47032 Bertinoro (FC). Participants will be hosted in the Centre guest quarters, (as an exception, in case of reduced availability of rooms in the Centre, they will be accommodated in local hotels).

Lectures and tutorials will be in English, with the following schedule:

Monday to Friday: lectures 9:00-13:00, 15:00-17:00; tutorials and individual hands-on sessions: 17:00-19:00.

Saturday: lectures 9:00-13:00


Fees and Enrollment

  •  Students, PhD students and temporary university staff: 850€
  •  University staff: 1000€ 
  •  Others: 2500€

Fee includes: accommodation (usually in doube room with breakfast and lunch starting from Sunday evening.

Application deadline   MAY 30th 2019
Fees Payment deadline   June 15th 2019

Programs are conditional to the recruitment of a minimum of 15 participants