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.

Cubadda, G., Grassi, S., Guardabascio, B. (2024). The time-varying Multivariate Autoregressive Index model. INTERNATIONAL JOURNAL OF FORECASTING [10.1016/j.ijforecast.2024.04.007].

The time-varying Multivariate Autoregressive Index model

Gianluca Cubadda
;
Stefano Grassi;Barbara Guardabascio
2024-01-01

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.
2024
Pubblicato
Rilevanza internazionale
Articolo
Esperti anonimi
Settore SECS-S/03
Settore STAT-02/A - Statistica economica
English
Con Impact Factor ISI
Large Vector Autoregressive Models Multivariate Autoregressive Index models Time-varying parameter models Reduced-rank regression Bayesian Vector Autoregressive Models
Cubadda, G., Grassi, S., Guardabascio, B. (2024). The time-varying Multivariate Autoregressive Index model. INTERNATIONAL JOURNAL OF FORECASTING [10.1016/j.ijforecast.2024.04.007].
Cubadda, G; Grassi, S; Guardabascio, B
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/362783
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