Training Calendar

France Stata Summer School: Time Series Forecasting and Machine Learning

Online 3 days (29th June 2020 - 1st July 2020) Stata Intermediate, Introductory
Delivered by: Laurent Ferrara, Skema Business School; Matteo Mogliani, Banque de France
Forecasting, Machine Learning, Summer School, Time series


This course will be delivered in French.

Forecasting in a time-series framework arises numerous issues, such as the choice of the model, the variables to be used, and the appropriate testing strategy. Recently, the collection and the availability of high-dimensional data has been posing new challenges to applied economists that classic regression approaches cannot address.

This course is designed to cover the basic topics in macroeconomic forecasting as well as advanced topics in Machine Learning for time-series analysis.

The course is split in two parts. The first part relates to linear regressions, time series models and economic forecasting. Non-linear time series models will be also introduced. The second part focuses on recent advances in machine learning and econometrics for the analysis of high-dimensional data, such as the concept of penalized likelihood, penalized regression models (Ridge, Lasso, and Elastic net), cross-validation and information criteria, and inference in penalized regressions.

Each course will be accompanied by practical sessions with Stata.

Course Agenda

*Please note this course will be delivered in French.

This comprehensive online course is delivered through the Zoom Webinar platform and runs over a total of 9 hours, with 4 hours each day (2-hours in the morning and 2-hours in the afternoon). Additional time is available at the end of the final session for open Q&A's.

Day 1:

Session 1: 11h00-13h00 (Paris Time)

Presentation of the models, estimation, tests and forecasting. Models to be considered: Linear regression, Auto-Regressive Distributed Lags and Non-linear time series models.

Session 2: 15h00-17h00 (Paris Time)

Empirical applications with data using Stata.

Day 2:

Session 1: 11h00-13h00 (Paris Time)

Introduction to penalized likelihood and Machine Learning in time-series econometrics. Review of fundamental penalized regressions (Ridge, Lasso, Elastic-Net). Selection of penalization parameters.

Session 2: 15h00-17h00 (Paris Time)

Empirical applications (estimation, forecasting) with cross-section and time-series data using Stata..

Q&A Session: 17h00-18h00 (Paris Time)


  • Basic knowledge of linear regression and time series of econometrics is assumed.
  • An introductory level of Stata helps but is not necessary.

Required Reading

  • Ghysels & Marcellino (2018), Applied Economic Forecasting using Time Series Methods, Oxford University Press
  • Hastie & Tibshirani,& Friedman (2009), The Elements of Statistical Learning (second edition), Springer Series in Statistics

Terms & Conditions

  • Student registrations: Attendees must provide proof of full time student status at the time of booking to qualify for student registration rate (valid student ID card or authorised letter of enrolment).
  • Additional discounts are available for multiple registrations.
  • Delegates are provided with temporary licences for the software(s) used in the course and will be instructed to download and install the software prior to the start of the course. (Alternatively, we can also provide laptops free of charge to attending delegates).
  • Payment of course fees required prior to the course start date.
  • Registration closes 5-calendar days prior to the start of the course.
    • 100% fee returned for cancellations made over 28-calendar days prior to start of the course.
    • 50% fee returned for cancellations made 14-calendar days prior to the start of the course.
    • No fee returned for cancellations made less than 14-calendar days prior to the start of the course.

The number of delegates is restricted. Please register early to guarantee your place.

  •  CommercialAcademicStudent
    29 June - 1 July 2020 - 11h-13h & 15h-17h (Paris Time) (29/06/2020 - 01/07/2020)

All prices exclude VAT or local taxes where applicable.

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