The Time-Varying Multivariate Autoregressive Index Model

52 Pages Posted: 10 Jan 2024

See all articles by Gianluca Cubadda

Gianluca Cubadda

University of Rome Tor Vergata - Department of Economics and Finance

Stefano Grassi

University of Rome Tor Vergata

Barbara Guardabascio

University of Perugia

Date Written: January 9, 2024

Abstract

Many economic variables are characterized by changes in their conditional mean and volatility, and time-varying Vector Autoregressive Models are often used to handle such complexity. Unfortunately, as the number of series grows, they present increasing estimation and interpretation issues. This paper tries to address this problem by proposing a Multivariate Autoregressive Index model that features time-varying mean and volatility. Technically, we develop a new estimation methodology that mixes switching algorithms with the forgetting factors strategy of Koop and Korobilis (2012). This substantially reduces the computational burden and allows one to select or weigh the number of common components, and other data features, in real-time without additional computational costs. Using US macroeconomic data, we provide a forecast exercise that shows the feasibility and usefulness of this model.

Keywords: Large Vector Autoregressive Models, Multivariate Autoregressive Index Models, Time-Varying Parameter Models, Bayesian Vector Autoregressive Models

Suggested Citation

Cubadda, Gianluca and Grassi, Stefano and Guardabascio, Barbara, The Time-Varying Multivariate Autoregressive Index Model (January 9, 2024). CEIS Working Paper No. 571, Available at SSRN: https://ssrn.com/abstract=4689088 or http://dx.doi.org/10.2139/ssrn.4689088

Gianluca Cubadda (Contact Author)

University of Rome Tor Vergata - Department of Economics and Finance ( email )

Via Columbia n.2
Roma, 00133
Italy

Stefano Grassi

University of Rome Tor Vergata ( email )

Via Cracovia 1
Rome, 00133
Italy

Barbara Guardabascio

University of Perugia ( email )

Via Pascoli 22
Perigoa, 06121
Italy

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