BGVAR (version 2.5.8)

avg.pair.cc: Average Pairwise Cross-Sectional Correlations

Description

Computes average pairwise cross-sectional correlations of the data and the country models' residuals.

Usage

avg.pair.cc(object, digits=3)

Value

Returns a list with the following elements

data.cor

is a matrix containing in the rows the cross-sections and in the columns the cross-sectional pairwise correlations of the data per variable.

resid.cor

is a matrix containing in the rows the cross-sections and in the columns the cross-sectional pairwise correlations of the country models' residuals per variable.

resid.corG

is a matrix containing in the rows the cross-sections and in the columns the cross-sectional pairwise correlations of the global models' residuals per variable. Only available when avg.pair.cc has been applied to a bgvar.res object from residuals.

data.res

is a summary object showing the number and percentage of correlations <0.1, between 0.1-0.2, 0.2-0.5 and <0.5 per variable of the data.

res.res

is a summary object showing the number and percentage of correlations <0.1, between 0.1-0.2, 0.2-0.5 and <0.5 per variable of the country models' residuals. This is also what is used by print.bgvar.

res.resG

is a summary object showing the number and percentage of correlations <0.1, between 0.1-0.2, 0.2-0.5 and <0.5 per variable of the global models' residuals. Only available when avg.pair.cc has been applied to a bgvar. res object from residuals.

Arguments

object

Either an object of class bgvar or residuals of class bgvar.res.

digits

Number of digits that should be used to print output to the console.

Author

Martin Feldkircher

Details

If used for analyzing the country models' residuals, avg.pair.cc computes for each country and a given variable, the average cross-sectional correlation (either for the data or for the residuals). In theory, including foreign variables should soak up cross-sectional residual dependence and correlation of the residuals should be small. Otherwise dynamic analysis, especially using GIRFs, might lead to invalid results. See Dees et al. (2007) for more details.

References

Dees, S., Di Mauro F., Pesaran, M. H. and Smith, L. V. (2007) Exploring the international linkages of the euro area: A global VAR analysis. Journal of Applied Econometrics, Vol. 22, pp. 1-38.

See Also

bgvar for estimation of a bgvar object. residuals for calculating the residuals from a bgvar object and creating a bgvar.res object.

Examples

# \donttest{
library(BGVAR)
data(testdata)
model.mn <- bgvar(Data=testdata,W=W.test,plag=1,SV=TRUE,
                  draws=100,burnin=100,prior="MN")
avg.pair.cc(model.mn)

res <- residuals(model.mn)
avg.pair.cc(res)
# }