This function calculates a complete generalized forecast error variance decomposition (GFEVDs) based on generalized impulse response functions akin to Lanne-Nyberg (2016). The Lanne-Nyberg (2016) corrected GFEVD sum up to unity.
gfevd(x, n.ahead=24, running=TRUE, applyfun=NULL, cores=NULL, verbose=TRUE)an object of class bgvar.
the forecast horizon.
Default is set to TRUE and implies that only a running mean over the posterior draws is calculated. A full analysis including posterior bounds is likely to cause memory issues.
Allows for user-specific apply function, which has to have the same interface than lapply. If cores=NULL then lapply is used, if set to a numeric either parallel::parLapply() is used on Windows platforms and parallel::mclapply() on non-Windows platforms.
Specifies the number of cores which should be used. Default is set to NULL and applyfun is used.
If set to FALSE it suppresses printing messages to the console.
Returns a list with two elements
GFEVD a three or four-dimensional array, with the first dimension referring to the K time series that are decomposed into contributions of K time series (second dimension) for n.ahead forecast horizons. In case running=TRUE only the posterior mean else also its 16% and 84% credible intervals is contained in the fourth dimension.
xglobal used data of the model.
Lanne, M. and H. Nyberg (2016) Generalized Forecast Error Variance Decomposition for Linear and Nonlinear Multivariate Models. Oxford Bulletin of Economics and Statistics, Vol. 78(4), pp. 595-603.