fnets (version 0.1.6)

factor.number: Factor number selection methods

Description

Methods to estimate the number of factor. When method = 'er', the factor number is estimated by maximising the ration of successive eigenvalues. When method = 'ic', the information criterion-methods discussed in Hallin and Liška (2007) (when fm.restricted = FALSE) and Alessi, Barigozzi and Capasso (2010) (when fm.restricted = TRUE) are implemented. The information criterion called by ic.op = 5 (as an argument to fnets or fnets.factor.model) is recommended by default.

Usage

factor.number(
  x,
  fm.restricted = FALSE,
  method = c("ic", "er"),
  q.max = NULL,
  center = TRUE
)

Value

S3 object of class factor.number. If method = "ic", a vector containing minimisers of the six information criteria, otherwise, the maximiser of the eigenvalue ratio

Arguments

x

input time series each column representing a time series variable; it is coerced into a ts object

fm.restricted

whether to estimate the number of restricted or unrestricted factors

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) when fm.restricted = TRUE or Avarucci et al. (2022) when fm.restricted = FALSE

q.max

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]))))

center

whether to de-mean the input x

Details

For further details, see references.

References

Ahn, S. C. & Horenstein, A. R. (2013) Eigenvalue ratio test for the number of factors. Econometrica, 81(3), 1203--1227.

Alessi, L., Barigozzi, M., and Capasso, M. (2010) Improved penalization for determining the number of factors in approximate factor models. Statistics & Probability Letters, 80(23-24):1806–1813.

Avarucci, M., Cavicchioli, M., Forni, M., & Zaffaroni, P. (2022) The main business cycle shock(s): Frequency-band estimation of the number of dynamic factors.

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.

Owens, D., Cho, H. & Barigozzi, M. (2024+) fnets: An R Package for Network Estimation and Forecasting via Factor-Adjusted VAR Modelling. The R Journal (to appear).

See Also

plot.factor.number, print.factor.number

Examples

library(fnets)
## Alessi, Barigozzi, and Capasso method for restricted models
abc <- factor.number(data.restricted, fm.restricted = TRUE)
print(abc)
plot(abc)

## Eigenvalue ratio method
er <- factor.number(data.restricted, method = "er", fm.restricted = TRUE)
print(er)
plot(er)

## Hallin and Liška method for unrestricted models
hl <- factor.number(data.unrestricted, fm.restricted = FALSE)
print(hl)
plot(hl)