MSBVAR (version 0.9-2)

dfev: Decompositions of Forecast Error Variance (DFEV) for VAR/BVAR/BSVAR models

Description

Computes the m dimensional decomposition of forecast error variance (DFEV) for a VAR, BVAR, and BSVAR models. User can specify the decomposition of the contemporaneous innovations.

Usage

dfev(varobj, A0 = NULL, k)

Arguments

varobj

VAR/BVAR/BSVAR object created from fitting a VAR/BVAR/BSVAR model using szbvar, szbsvar, or reduced.form.var.

A0

Decomposition of the contemporaneous error covariance matrix. Default is to use the (lower) Cholesky decomposition of the residual error covariance matrix for VAR and BVAR models, or the inverse of $A(0)$ in B-SVAR models.

k

Number of periods over which to compute the deccomposition.

Value

Returns a list with

errors

M x M x K of the percentage of the innovations in variable i explained by the other M variables.

std.err

M x k dimension matrix of the forecast standard errors.

names

Variable names

Details

The decomposition of the forecast error variance (DFEV) provides a measure of the relationship among forecast errors or impact of shocks to a VAR/BVAR/BSVAR system. It is computed by finding the moving average representation (MAR) of the VAR/BVAR/BSVAR model and then tracing out the path of innovations through the system. For each of the M innovations in a VAR/BVAR/BSVAR, the amount of the variance in these forecast errors or innovations is computed and returned in a table. The table can be accessed via the print.dfev and summary.dfev functions.

References

Brandt, Patrick T. and John T. Williams. Multiple Time Series Models. Thousand Oaks, CA; Sage Press.

See Also

See also print.dfev and summary.dfev for a nicely formatted tables and an output example

Examples

data(IsraelPalestineConflict)
varnames <- colnames(IsraelPalestineConflict)
fitted.BVAR <- szbvar(IsraelPalestineConflict, p=6, z=NULL,
                       lambda0=0.6, lambda1=0.1,
                       lambda3=2, lambda4=0.25, lambda5=0, mu5=0,
                       mu6=0, nu=3, qm=4, prior=0,
                       posterior.fit=FALSE)

A0 <- t(chol(fitted.BVAR$mean.S))
dat.dfev <- dfev(fitted.BVAR, A0, 24)

print(dat.dfev)
summary(dat.dfev)