Representation, estimation and forecasting of the multivariate index-augmented autoregressive model

Gianluca Cubadda and Barbara Guardabascio

International Journal of Forecasting, 2019, vol. 35, issue 1, 67-79

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 a few linear combinations of all the variables in the system. We call this multivariate index-augmented autoregression (MIAAR) modelling. 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 increases, we propose a regularized version of our algorithm for handling a medium–large number of time series. We illustrate the usefulness of the MIAAR modelling by both empirical applications and simulations.

Keywords: Multivariate autoregressive index models; Reduced rank regression; Dimension reduction; Shrinkage estimation; Macroeconomic forecasting (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (14)

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Related works:
Working Paper: Representation, Estimation and Forecasting of the Multivariate Index-Augmented Autoregressive Model (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:35:y:2019:i:1:p:67-79

DOI: 10.1016/j.ijforecast.2018.08.002

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