mvgam_fevd-class: 'mvgam_fevd' object description

In nicholasjclark/mvgam: Multivariate (Dynamic) Generalized Additive Models

mvgam_fevd object description

Description

A mvgam_fevd object returned by function fevd(). Run methods(class = "mvgam_fevd") to see an overview of available methods.

Details

A forecast error variance decomposition is useful for quantifying the amount of information each series that in a Vector Autoregression contributes to the forecast distributions of the other series in the autoregression. This object contains the forecast error variance decomposition using the orthogonalised impulse response coefficient matrices \Psi_h, which can be used to quantify the contribution of series j to the h-step forecast error variance of series k:

\sigma_k^2(h) = \sum_{j=1}^K(\psi_{kj, 0}^2 + \ldots + \psi_{kj, h-1}^2) \quad

If the orthogonalised impulse reponses (\psi_{kj, 0}^2 + \ldots + \psi_{kj, h-1}^2) are divided by the variance of the forecast error \sigma_k^2(h), this yields an interpretable percentage representing how much of the forecast error variance for k can be explained by an exogenous shock to j. This percentage is what is calculated and returned in objects of class mvgam_fevd, where the posterior distribution of variance decompositions for each variable in the original model is contained in a separate slot within the returned list object

Author(s)

Nicholas J Clark

References

Lütkepohl, H (2006). New Introduction to Multiple Time Series Analysis. Springer, New York.

See Also

mvgam(), VAR()

nicholasjclark/mvgam documentation built on April 17, 2025, 9:39 p.m.