Loss based prior for the degrees of freedom of the Wishart distribution

TL;DR

This paper introduces a loss-based prior for the degrees of freedom in the Wishart distribution within VAR models, enhancing forecasting accuracy by accounting for uncertainty in parameter estimation.

Contribution

It proposes a novel hyperprior for degrees of freedom using a loss-based approach, improving Bayesian VAR modeling for macroeconomic and health data.

Findings

Improved forecast accuracy in macroeconomic data

Effective modeling of Dengue infection data

Demonstrated advantages over traditional fixed degrees of freedom

Abstract

Motivated by the proliferation of extensive macroeconomic and health datasets necessitating accurate forecasts, a novel approach is introduced to address Vector Autoregressive (VAR) models. This approach employs the global-local shrinkage-Wishart prior. Unlike conventional VAR models, where degrees of freedom are predetermined to be equivalent to the size of the variable plus one or equal to zero, the proposed method integrates a hyperprior for the degrees of freedom to account for the uncertainty about the parameter values. Specifically, a loss-based prior is derived to leverage information regarding the data-inherent degrees of freedom. The efficacy of the proposed prior is demonstrated in a multivariate setting for forecasting macroeconomic data, as well as Dengue infection data.