[R] Need some help in R programming code

[saikat sarkar]

Dear R guru,

I am Saikat Sarkar working as a researcher of Economics in Tampere
University, Finland. I am trying to estimate some Garch related tests with
Bayesian analysis by R programme.

I am not good in R but trying to survive.

Anyway I have the coding but not working properly. I have tried to find the
problem but failed. I am writing to all R gurus to help me out.

Could you please look at the problem and help me if you can.

Thanking you
saikat

Could you please look at the problem below:

R message is like below:
-------
Error in garch.gjr.d0(param, y, X, Z, iterate = FALSE)$res :
  $ operator is invalid for atomic vectors
plot(Ch.mic,Ch.mic.rep,xlim=c(0,6),ylim=c(0,6))
-----------------------
Coding
--------

res<-garch.gjr.d0(a$estimate,y,X,X,iterate=FALSE)$res
sig2<-garch.gjr.d0(a$estimate,y,X,X,iterate=FALSE)$sig2


test.mic <- function(param,y,X,Z)

{
   n <- length(y)

   res <- garch.gjr.d0(param,y,X,Z,iterate=FALSE)$res

   s2 <- garch.gjr.d0(param,y,X,Z,iterate=FALSE)$sig2

   s<-sqrt(s2)

   r <- res

   u <- r[-1]

   r2<- (r[-n]^2*s2[-n])/s[-1]

   r3<- (r[-n]^3*s2[-n]*s[-n])/s[-1]

   U <- cbind(r[-n],r2,r3)

   U <- cbind(1,U)

   U <- U/matrix(s[-1],nr=nrow(U),nc=ncol(U))

   Fstat<-summary(lm(u~U-1))$fstatistic[1]

   Fstat

}


sim.gjr <- function(par,y,X=0,Z=0)
{

   n <- length(y)

   e<-numeric(n)

   yrep<-numeric(n)

   yrep[1] <- y[1]

   n.dummies.mean <- dim(X)[2]

   if(is.matrix(X)) e[1] <- e[1]-sum(X[1,]*par[9:(8+n.dummies.mean)])

   delta <- par[-(1:(8+n.dummies.mean))]

   s2 <- var(y)

   for(i in 2:n){

     s2 <-
par[5]+par[6]*e[i-1]^2+par[7]*ifelse(e[i-1]<0,1,0)*e[i-1]^2+par[8]*s2+sum(X[i,]*delta)

     e[i] <- rnorm(1,0,sqrt(s2))

     yrep[i]<-par[1]+par[2]*yrep[i-1]+par[3]*s2+par[4]*yrep[i-1]*s2+e[i]

   }


   if(is.matrix(X)) yrep <- yrep + X%*%par[9:(8+n.dummies.mean)]

   yrep
}

yrep <- sim.gjr(a$estimate,y,X,Z)


n.test <- 500

Ch.mic <- Ch.mic.rep <- numeric(n.test)

for(i in 1:n.test){

   simulation <- sample(1:10000,1)

   par <- sims.matrix[simulation,]

   Ch.mic[i] <- test.mic(par,y,X,X)

   yrep <- sim.gjr(par,y,X,X)

   Ch.mic.rep[i] <- test.mic(par,yrep,X,X)
}

 plot(Ch.mic,Ch.mic.rep,xlim=c(0,6),ylim=c(0,6))

 lines(c(0,6),c(0,6)) 
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