dyn.pca: Dynamic PCA

Description

Performs principal components analysis in frequency domain for identifying common and idiosyncratic components.

Usage

dyn.pca(
  xx,
  q = NULL,
  q.method = c("ic", "er"),
  ic.op = 5,
  kern.bw = NULL,
  mm = NULL
)

Value

a list containing

q: number of factors
q.method.out: if q = NULL, the output from the chosen q.method, either a vector of eigenvalue ratios or hl.factor.number
spec: a list containing the estimates of the spectral density matrices for x, common and idiosyncratic components
acv: a list containing estimates of the autocovariance matrices for x, common and idiosyncratic components
kern.bw: input parameter

Arguments

xx

centred input time series matrix, with each row representing a variable

q

number of factors. If q = NULL, the factor number is estimated by an information criterion-based approach of Hallin and Liška (2007)

q.method

A string specifying the factor number selection method; possible values are:

"ic": information criteria-based methods of Alessi, Barigozzi & Capasso (2010) when fm.restricted = TRUE or Hallin and Liška (2007) when fm.restricted = FALSE

"er"

eigenvalue ratio of Ahn and Horenstein (2013)

ic.op

choice of the information criterion penalty. Currently the three options from 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

kern.bw

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)))

bandwidth