EcoSta 2021: Start Registration
View Submission - EcoSta2021
A0719
Title: Sparseness, consistency and model selection for Markov regime-switching autoregressives Authors:  Abbas Khalili - McGill University (Canada) [presenting]
David Stephens - McGill University (Canada)
Abstract: Markov regime-switching Gaussian autoregressive models, which aim to capture temporal heterogeneity exhibited by time series data, are studied. In constructing a Markov regime-switching model, several specifications must be made relating to both the state and observation models; in particular, the complexity of these models must be specified when fitting to a dataset. We propose new regularization methods based on the conditional likelihood for simultaneous autoregressive-order and parameter estimation with the number of regimes fixed and use a regularized Bayesian information criterion to select the number of regimes. Unlike the existing information-theoretic approaches, the new methods avoid an exhaustive search of the model space for model selection and thereby are computationally more efficient. We establish large sample properties of the proposed methods for estimation, model selection, and forecasting. We also evaluate the finite sample performance of the methods via simulations and illustrate their applications by analyzing a real dataset.