A0721
Title: Sparse estimation in Markov regime-switching models
Authors: Abbas Khalili - McGill University (Canada) [presenting]
Gilberto Chavez Martinez - McGill University (Canada)
Abstract: Markov regime-switching models are widely used to model heterogeneous and complex relationships in multivariate time series. While maximum likelihood estimation (MLE) is the standard approach for parameter estimation in these models, it often becomes unstable even in moderate parameter space dimensions. A class of regularization-based estimators, designed to address this challenge, is presented. Both the theoretical properties and finite-sample performance of the proposed methods are discussed, followed by an application to real data.
