Representation, Estimation and Forecasting of the Multivariate Index-Augmented Autoregressive Model

Abstract

We examine the conditions under which each individual series that is generated by a vector autoregressive model can be represented as an autoregressive model that is augmented with the lags of few linear combinations of all the variables in the system. We call this modelling Multivariate Index-Augmented Autoregression (MIAAR). We show that the parameters of the MIAAR can be estimated by a switching algorithm that increases the Gaussian likelihood at each iteration. Since maximum likelihood estimation may perform poorly when the number of parameters gets larger, we propose a regularized version of our algorithm to handle a medium-large number of time series. We illustrate the usefulness of the MIAAR modelling both by empirical applications and simulations.

Keywords: Multivariate autoregressive index models, reduced rank regression, dimension reduction, shrinkage estimation, macroeconomic forecasting.

JEL Classification: C32