FIAR (version 0.6)

pdiffGranger: partial difference Granger causality

Description

Compute partial difference conditional Granger causality of multivariate timeseries.

Usage

pdiffGranger(data, nx = 1, ny = 1, order=1, perm = FALSE, bs = 100)

Arguments

data

object containing all observations (rows) and variables (columns) that are being considered. The variables should be ordered as follows: First the variables that are supposed to Granger cause a set of other variables (>=1). Then the set of variables (>=1) that are Granger caused by the first set of variables. Finally, a set of variables to condition on(>=1).

nx

The number of variables (>=1) that are supposed to Granger cause a set of other variables (default = 1), conditioned on a third set of variables (>=1).

ny

The number of variables (>=1) that are supposed to be Granger caused by the first nx variables (default = 1), conditioned on a third set of variables (>=1).

order

Autoregressive order (>=1) of timeseries. Can be computed using ARorder().

perm

Logical. If perm = FALSE (default), only the Granger causality measure is produced. If perm = TRUE, the Granger test is computed and a permutation is performed to generate the H0 distribution.

bs

Number of permutation samples. Default=100

Value

Partial difference Granger causality measure and p value.

Details

The total linear dependence between X and Y can be divided in three components: a directed influence from X to Y, a directed influence from Y to X and an undirected instantaneous influence between them. The difference Granger causality from X to Y computed in the function diff.Granger() subtracts the partial conditional Granger causality from Y to X from the partial conditional Granger causality from X to Y. This can be used as a measure of how much stronger (weaker) one directed influence is compared to the opposite directed influence.

See Also

diffGranger

Examples

# NOT RUN {
# Example data with 5 regions x, y, z, q, w
head(grangerdata)

# Calculate AR() order of the data
ARorder(grangerdata, max=10)
 
# Compute partial difference conditional Granger causality of region x to 
# regions y and z, conditional on regions q and w
F <- pdiffGranger(grangerdata, nx=1, ny=2, order=3)

# Compute F and permutation H0 distribution
F <- pdiffGranger(grangerdata, nx=1, ny=2, order=3, perm=TRUE, bs=50)
# }