Granger causality

granger.R

library(lmtest)

data(ChickEgg)
chickegg <- as.data.frame(ChickEgg)
chickegg$Year <- 1:nrow(chickegg) + 1929
attach(chickegg)

library(forecast)
ndiffs(chicken, alpha=0.05, test=c("kpss"))
ndiffs(egg, alpha=0.05, test=c("kpss"))

dchick <- diff(chicken)
degg <- diff(egg)

plot.ts(dchick)
plot.ts(degg)


grangertest(dchick ~ degg, order=)



Find_Max_CCF<- function(a,b)
{
	d <- ccf(a, b, plot = FALSE)
	cor = d$acf[,,1]
	lag = d$lag[,,1]
	res = data.frame(cor,lag)
	res_max = res[which.max(res$cor),]
	return(res_max)
} 


Find_Max_CCF(dchick, degg)


library(vars)
data(Canada)
var.2c <- VAR(Canada, p = 2, type = "const")
a <- causality(var.2c, cause = "e")
name 
#use a robust HC variance-covariance matrix for the Granger test:
causality(var.2c, cause = "e", vcov.=vcovHC(var.2c))
 
#use a wild-bootstrap procedure to for the Granger test
 
causality(var.2c, cause = "e", boot=TRUE, boot.runs=1000)## End(Not run)

Hi
I have a question, can we have Nonlinear Granger causality Codes according to this?