TRAINING SCHOOL

The Training School on Econometrics for the Environment, organized in conjunction with the CE² 2026 International Conference, is designed to provide PhD students, early-career researchers, and practitioners with advanced theoretical and practical skills for analyzing environmental, climate, energy, and natural resource issues.

The programme will focus on three major methodological areas: Structural Equation Modeling, Panel Econometrics, and Spatial Econometrics. Through a combination of lectures and hands-on applications using relevant statistical software and real-world data, participants will gain practical experience in applying these modern econometric approaches to environmental and sustainability challenges.

The Training School will provide an excellent opportunity to strengthen research capacities, exchange ideas with leading international scholars, and develop innovative empirical approaches for addressing complex environmental and climate-related issues.

Lecture #1: Panel Econometrics

December 15th, 2026

Title: “....”

By ..............................

 Lecture #2: Spatial Econometrics

December 15th, 2026

Title: “....”

By ..............................

This course is a lecture-based introduction to the methodology and application of dynamic panel econometric models for Master and Ph.D. students.  

The objective of the course is to provide an introduction to . Students will learn how to model and incorporate spatial dependencies into their empirical analyses.

The course will cover topics such as: ..

Bibliography (tba):

English:

1.  Elhorst, J. P. (2014): Spatial econometrics : from cross-sectional data to spatial panels, Springer.

2.  LeSage and Pace (2009), Introduction to spatial econometrics, s. Boca Raton, Taylor & Francis;

3.  Anselin L. (1988), Spatial econometrics: Methods and models. Kluwer Academic Publishers.

French:

1.  Le Gallo J. (2004), Hétérogénéité spatiale, principes et méthodes, Economie et Prévision, vol. 162, pp. 151-172.

2. Le Gallo J. (2002), Econométrie spatiale : l’autocorrélation spatiale dans les modèles de régression linéaire, Economie  et Prévision, vol. 155, pp. 139-158.

3. Jayet H. (2001), Econométrie des données spatiales. Une introduction à la pratique, Cahiers d’Economie et de Sociologie Rurale, vol. 58-59, pp. 105-129.

          https://www.insee.fr/en/information/3635545

Lecture #3: Structural Equation Modeling

December 18th, 2026

Title: “....”

By ..............................