TY - JOUR

T1 - Loss-based prior for the degrees of freedom of the Wishart distribution

AU - Rossini, Luca

AU - Villa, Cristiano

AU - Prevenas, Sotiris

AU - McCrea, Rachel

PY - 2024/4/5

Y1 - 2024/4/5

N2 - 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 in 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 both for forecasting macroeconomic data, and Dengue infection data.

AB - 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 in 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 both for forecasting macroeconomic data, and Dengue infection data.

U2 - 10.1016/j.ecosta.2024.04.001

DO - 10.1016/j.ecosta.2024.04.001

M3 - Journal article

JO - Econometrics and Statistics

JF - Econometrics and Statistics

SN - 2452-3062

ER -