**Partners involved**

Univ. of Cyprus (Local Organizer), Univ. “La Sapienza” Roma, Univ. of Klagenfurt.

**Motivation and Objectives**

Model selection is a central issue in statistical and econometrical modelling. The tutorial will describe classical methods and recent advances in statistical model selection. Computational aspects will be emphasized. A brief introduction to conventional approaches to model selection and their limitations will be provided. The tutorial will focus on recent advances in tackling large-scale and real-world model selection problems. This will involve the use of optimization techniques and heuristics for computing the best subset models for linear econometric models.

**Organization**

Two days with seven lectures of 90 minutes and several short (60 minutes) sessions in the computer laboratory.

- Statistical foundation for variable selection: Model selection strategies for simple regression models; Bayesian model selection.
- Foundations of macroeconometric modelling: Structural models, SVAR models, rational expectations models, time series models: ARMA, ARIMA, VAR models.
- Principles of model analysis: Model estimation philosophies, ex-post and ex-ante (stochastic) simulation, dynamic multipliers, model evaluation and comparison.
- Computational and numerical efficient methods for estimating and updating (non)linear models, e.g., matrix factorization and combinatorial optimization.
- Regression trees and graphs for computing the best subset regression models: Branch-and-bound strategy and specific software developed for this purpose.
- Heuristic strategies for large-scale model selection problems: Heuristic branch-and-bound strategies; genetic algorithms and neural networks.
- Design of parallel algorithms for model selection.