Estimates the number of factors by minimising an information criterion over sub-samples of the data.
Currently the three information criteria proposed in Hallin and Liška (2007) (ic.op = 1, 2 or 3)
and their variations with logarithm taken on the cost (ic.op = 4, 5 or 6) are implemented,
with ic.op = 5 recommended as a default choice based on numerical experiments.
hl.factor.number(x, q.max = NULL, mm = NULL, center = TRUE)a list containing
a vector containing minimisers of the six information criteria
input time series matrix, with each row representing a variable
maximum number of factors; if q.max = NULL, a default value is selected as min(50, floor(sqrt(min(dim(x)[2] - 1, dim(x)[1]))))
a positive integer specifying the kernel bandwidth for dynamic PCA; by default, it is set to floor(4 *(dim(x)[2]/log(dim(x)[2]))^(1/3)))
whether to de-mean the input x row-wise
See Hallin and Liška (2007) for further details.
Hallin, M. & Liška, R. (2007) Determining the number of factors in the general dynamic factor model. Journal of the American Statistical Association, 102(478), 603--617.