Advanced statistical models - Models for panel data

Luca Trapin

  • Date:

    17 MARCH
    -
    31 MARCH 2026
     from 9:00 to 13:00

  • Event location: Aula IV, Department of Statistical Sciences, Via Belle Arti 41 (STAT PhD Classes Virtual Room on need)

  • Type: Cycle 41 - Mandatory Courses

Aims: To introduce advanced modelling strategies for independent data or data characterized by spatial or temporal dependence including model selection, inference, prediction.

Learning outcomes: At the end of the course the students will have an appreciation of the different modelling strategies for data characterized by various kinds of dependencies and will be able to deal with estimation issues, to perform model selection and to apply the models to real data situations.

 

Module 2. Models for panel data

- Large N, Small T: 

  • Static panels: pooled, fixed effect, random effect estimators
  • Dynamic panels: endogeneity, IV, and gmm estimators

 - Large N, Large T: 

  • Interactive fixed effect estimators
  • Grouped panels

 - Nonlinear panels