Maximum likelihood estimation in vector autoregressive models with multivariate scaled t-distributed innovations using EM-based algorithms

• Published in: Communications in statistics. Simulation and computation Vol. 47; no. 3; pp. 890 - 904
• Main Authors: Mirniam, A. S., Nematollahi, A. R.
• Format: Journal Article
• Language: English
• Published: Philadelphia Taylor & Francis 16.03.2018; Taylor & Francis Ltd
• Subjects: Algorithms; Autoregressive models; Computer simulation; ECM algorithm; ECME algorithm; Economic models; EM algorithm; Maximum likelihood estimates; Maximum likelihood estimation; Multivariate scaled t-distribution; Parameter estimation; Regression analysis; Run time (computers); Vector autoregressive process
• ISSN: 0361-0918; 1532-4141
• DOI: 10.1080/03610918.2017.1295155

This article is concerned with the likelihood-based inference of vector autoregressive models with multivariate scaled t-distributed innovations by applying the EM-based (ECM and ECME) algorithms. The ECM and ECME algorithms, which are analytically quite simple to use, are applied to find the maximum likelihood estimates of the model parameters and then compared based on the computational running time and the accuracy of estimation via a simulation study. The results demonstrate that the ECME is efficient and usable in practice. We also show how the method can be applied to a multivariate dataset.