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

Gianluca Cubadda and Barbara Guardabascio

No 397, CEIS Research Paper from Tor Vergata University, CEIS

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. (search for similar items in EconPapers)
JEL-codes: C32 (search for similar items in EconPapers)
Pages: 26 pages
Date: 2017-02-07, Revised 2018-07-13
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for and nep-ore
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Citations: View citations in EconPapers (4)

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Related works:
Journal Article: Representation, estimation and forecasting of the multivariate index-augmented autoregressive model (2019)
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