Seyed Yaser Samadi (Southern Illinois University)
Title: Reduced-Rank Envelope Vector Autoregressive Models
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Abstract:
Classical vector autoregressive (VAR) models have long been a popular choice for modeling multivariate time series data due to their flexibility and ease of use. However, the VAR model suffers from overparameterization which is a serious issue for high-dimensional time series data as it restricts the number of variables and lags that can be incorporated into the model. Several statistical methods have been proposed to achieve dimension reduction in the parameter space of VAR models. Yet, these methods prove inefficient in extracting relevant information from complex datasets, as they fail to distinguish between information aligned with scientific objectives and are also inefficient in addressing rank deficiency problems. Envelope methods, founded on novel parameterizations that employ reduced subspaces to establish connections between the mean function and covariance matrix, offer a solution by efficiently identifying and eliminating irrelevant information. In this presentation, we introduce a new, parsimonious VAR model that incorporates the concept of envelope models into the reduced-rank VAR framework that can achieve substantial dimension reduction and efficient parameter estimation. We will present the results of simulation studies and real data analysis comparing the performance of our proposed model with that of existing models in the literature.
Speaker
S. Yaser Samadi obtained his PhD in Statistics from the University of Georgia in 2014. He was a Research Fellow at SAMSI & Duke University in 2020-2021. He is currently an Associate Professor at Southern Illinois University Carbondale, IL.