Hi, I am brand new to R and not familiar with the language, though I
have been reading the manuals and making some slow going progress. I am
working with some source code from a Global Vector Auto -Regressive
program written by Ranier Puhr from the R-forge group. I need help
interpreting the processes of the following code.
I am going to
post in parts since it's pretty long:
GVAR <- function (data, tw = NULL, p, q = p, r = NULL, weight, case,
exo.var = FALSE,
d = NULL, endo = NULL, ord = NULL, we = NULL,
method = "max.eigen")
# data ... timeseries data as list (each entry is a matrix of a
subsystem of variables,
# if exo.var=TRUE the last entry are exogeneous variables)
# tw ... time window, vector of start and end point
# p ... scalar/vector of endogenous lags, (N+1)x1
# q ... scalar/vector of weakly exogeneous lags, (N+1)x1
# r ... vector of cointegrating relations
# weight ... weight matrix of dimension (N+1)x(N+1)
# case ... scalar/vector of cases ("I" to "V"), (N+1)x1
# endo ... list of endogenous variables used
# ord ... list showing the same variables for weakly exogeneous analysis
# we ... list with numbers of weakly exogeneous variables included in
each VECM,
# corresponds to numbers in ord
# exo.var ... if TRUE strictly exogeneous variables are included in the
model
# d ... list showing which strictly exogeneous variables enter the
subsystem equations
# lex ... scalar/vector of lags of exogenous variables
# method ... select cointegrating rank by max. eigenvalue ("max.eigen") or
trace statistic ("trace")
# ----- Set subsystems -----
cmodel <- list()
N <-
length(data)-1 # number of
subsystems i=0,1,...,N
dims <- vector()
for (i in 1:(N+1))
{
if (!is.null(dim(data[[i]])))
{
dims[i] <- dim(data[[i]])[1]
} else {
dims[i]
<- length(data[[i]])
}
}
max.dim <- max(dims)
tsi <- tsp(data[[((1:length(dims))[dims==max(dims)])[1]]])
if (is.null(tw))
{
start.ts <- tsi[1]
end.ts
<- tsi[2]
} else {
start.ts <- tw[1]
end.ts
<- tw[2]
}
freq <- tsi[3]
dt <- 1/freq
n.exo <- 0
ex <- 0
n.ex <- rep(0,N+1)
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