Coint  R Documentation 
Coint
seeks for a contemporaneous linear
transformation for a multivariate time series such that we can identifying
cointegration rank from the transformed series.
Coint(
Y,
lag.k = 5,
type = c("acf", "pptest", "Chang", "all"),
c0 = 0.3,
m = 20,
alpha = 0.01
)
Y 

lag.k 
Time lag
where 
type 
The method of identifying cointegration rank after segment
procedure. Option is 
c0 
The prescribed constant for identifying
cointegration rank using 
m 
The prescribed constant for identifying
cointegration rank using 
alpha 
The prescribed significance level for identifying
cointegration rank using 
An object of class "coint" is a list containing the following components:
Z 
The transformed series with 
coint_rank 
A 
lag.k 
a prescribed positive integer which means the time lags used to calculate the statistic. 
method 
a character string indicating which method was performed. 
Zhang, R., Robinson, P. & Yao, Q. (2019). Identifying Cointegration by Eigenanalysis. Journal of the American Statistical Association, Vol. 114, pp. 916–927
p < 10
n < 1000
r < 3
d < 1
X < mat.or.vec(p, n)
X[1,] < arima.sim(nd, model = list(order=c(0, d, 0)))
for(i in 2:3)X[i,] < rnorm(n)
for(i in 4:(r+1)) X[i, ] < arima.sim(model = list(ar = 0.5), n)
for(i in (r+2):p) X[i, ] < arima.sim(n = (nd), model = list(order=c(1, d, 1), ar=0.6, ma=0.8))
M1 < matrix(c(1, 1, 0, 1/2, 0, 1, 0, 1, 0), ncol = 3, byrow = TRUE)
A < matrix(runif(p*p, 3, 3), ncol = p)
A[1:3,1:3] < M1
Y < t(A%*%X)
Coint(Y, type = "all")
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