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Factor models with vector autoregressive dynamics

Grant number: 23/11547-2
Support Opportunities:Scholarships in Brazil - Doctorate (Direct)
Effective date (Start): September 01, 2023
Effective date (End): July 31, 2027
Field of knowledge:Physical Sciences and Mathematics - Probability and Statistics - Statistics
Principal Investigator:Pedro Alberto Morettin
Grantee:Davi Oliveira Chaves
Host Institution: Instituto de Matemática e Estatística (IME). Universidade de São Paulo (USP). São Paulo , SP, Brazil
Associated research grant:23/02538-0 - Time series, wavelets, high dimensional data and applications, AP.TEM

Abstract

Factor Models are extensively used as a resource to reduce the dimensionality (number of variables) and to explain the common variability in multivariate data sets. The idea, like in Principal Component analysis, is to consider a small set of factors, which explain most of the variance in the data. In the context of multivariate time series, the analysis of the covariance structure becomes more difficult, especially when the time series are non-stationary, show co-movements, time transitions and structural changes. In this Project we propose a factor model where the factors follow a vector autoregressive dynamics and the coefficients are time-varying and, as a consequence, the variances are also time-varying. Relevance: This research line lies in the state-of-art in problems involving multivariate non-stationary time series, with applications in finance, macroeconomic data in presence of transitions between economic recessions or the impact of political crises. (AU)

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