Estimation and Forecasting in High-Dimensional Dynamic Factor Models in the Presence of Outliers

• Grant number: 25/07456-7
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: July 01, 2025
• End date: December 31, 2025
• Field of knowledge: Physical Sciences and Mathematics - Probability and Statistics - Statistics
• Principal Investigator: Luiz Koodi Hotta
• Grantee: Cauã Pereira Masseu
• Host Institution: Instituto de Matemática, Estatística e Computação Científica (IMECC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
• Associated research grant: 23/02538-0 - Time series, wavelets, high dimensional data and applications, AP.TEM

Abstract

¿The project investigates the robustness of dynamic factor models applied to time series, with an emphasis on the presence of outliers. It evaluates the performance of the outlier detection method proposed by Galeano and Peña (2024) (Detecting outliers in high-dimensional time series by dynamic factor models), in terms of estimation and forecasting effectiveness. The methodological approach combines controlled simulations and empirical application to real data. The objective is to study the predictive accuracy and robustness of these models in the face of various types of outliers, providing support for theoretical and practical advancements. The results obtained are expected to provide a solid foundation for future postgraduate-level investigations.¿