View source: R/CP_functions_unified.R
CP_MTS  R Documentation 
CP_MTS()
deals with CPdecomposition for highdimensional
matrix time series proposed in Chang et al. (2023):
{\bf{Y}}_t = {\bf A \bf X}_t{\bf B}^{'} +
{\boldsymbol{\epsilon}}_t,
where {\bf X}_t = diag(x_{t,1},\ldots,x_{t,d})
is an d \times d
latent process, {\bf A}
and {\bf B}
are , respectively, p
\times d
and q \times d
unknown constant matrix, and {\boldsymbol{\epsilon}}_t
is a p \times q
matrix white noise process. This function aims to estimate the rank
d
and the coefficient matrices {\bf A}
and {\bf B}
.
CP_MTS(
Y,
xi = NULL,
Rank = NULL,
lag.k = 15,
lag.ktilde = 10,
method = c("CP.Direct", "CP.Refined", "CP.Unified")
)
Y 
A 
xi 
A 
Rank 
A list of the rank 
lag.k 
Integer. Time lag
,
where 
lag.ktilde 
Integer. Time lag

method 
Method to use: 
An object of class "mtscp" is a list containing the following components:
A 
The estimated 
B 
The estimated 
f 
The estimated latent process 
Rank 
The estimated rank 
Chang, J., He, J., Yang, L. and Yao, Q.(2023). Modelling matrix time series via a tensor CPdecomposition. Journal of the Royal Statistical Society Series B: Statistical Methodology, Vol. 85(1), pp.127–148.
Chang, J., Du, Y., Huang, G. and Yao, Q.(2024+). On the Identification and Unified Estimation Procedure for the Matrix CPfactor Model, Working paper.
p = 10
q = 10
n = 400
d = d1 = d2 = 3
data < DGP.CP(n,p,q,d1,d2,d)
Y = data$Y
res1 < CP_MTS(Y,method = "CP.Direct")
res2 < CP_MTS(Y,method = "CP.Refined")
res3 < CP_MTS(Y,method = "CP.Unified")
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