Maximum Likelihood Estimation of the Vector AutoRegressive To Anything (VARTA) model

TL;DR

This paper introduces the VARTA model, a flexible approach for multivariate time series that captures diverse distributional properties and improves forecasting distributions beyond traditional Gaussian VAR models.

Contribution

It develops a maximum likelihood estimator for the VARTA model, enabling better modeling of complex multivariate time series distributions.

Findings

The estimator performs well in simulations.

The model provides improved forecasting distributions.

Diagnostic tools are proposed for model assessment.

Abstract

The literature on multivariate time series is, largely, limited to either models based on the multivariate Gaussian distribution or models specifically developed for a given application. In this paper we develop a general approach which is based on an underlying, unobserved, Gaussian Vector Autoregressive (VAR) model. Using a transformation, we can capture the time dynamics as well as the distributional properties of a multivariate time series. The model is called the Vector AutoRegressive To Anyting (VARTA) model and was originally presented by Biller and Nelson (2003) who used it for the purpose of simulation. In this paper we derive a maximum likelihood estimator for the model and investigate its performance. We also provide diagnostic analysis and how to compute the predictive distribution. The proposed approach can provide better estimates about the forecasting distributions which can be of every kind not necessarily Gaussian distributions as for the standard VAR models.