The Time-Varying Multivariate Autoregressive Index Model

TL;DR

This paper introduces a novel time-varying multivariate autoregressive index model that efficiently handles large economic datasets with changing means and volatility, improving estimation and interpretability.

Contribution

It develops a new estimation approach combining switching algorithms with forgetting factors, enabling real-time model selection and averaging without high computational costs.

Findings

Demonstrates the model's effectiveness on USA macroeconomic data

Shows improved forecasting accuracy over traditional models

Provides structural insights into economic variable dynamics

Abstract

Many economic variables feature changes in their conditional mean and volatility, and Time Varying Vector Autoregressive Models are often used to handle such complexity in the data. Unfortunately, when the number of series grows, they present increasing estimation and interpretation problems. This paper tries to address this issue proposing a new Multivariate Autoregressive Index model that features time varying means and volatility. Technically, we develop a new estimation methodology that mix switching algorithms with the forgetting factors strategy of Koop and Korobilis (2012). This substantially reduces the computational burden and allows to select or weight, in real time, the number of common components and other features of the data using Dynamic Model Selection or Dynamic Model Averaging without further computational cost. Using USA macroeconomic data, we provide a structural analysis and a forecasting exercise that demonstrates the feasibility and usefulness of this new model. Keywords: Large datasets, Multivariate Autoregressive Index models, Stochastic volatility, Bayesian VARs.