Description Usage Arguments Value Author(s) References Examples
The function applies the two-stage methodology for multiple change-point detection under factor modelling. It first transforms the input time series into panels of statistics that contain the change-points in the second-order structure of the common and idiosyncratic components, as change-points in their ‘means’, to which the Double CUSUM Binary Segmentation algorithm is applied in the second stage. The function returns change-point estimates from the common and idiosyncratic components separately.
1 2 3 4 |
x |
input time series matrix, with each row representing a time series |
r |
the number of factors, if |
bn.op |
an index number for the information criterion-based estimator of Bai and Ng (2002)
for the number of factors, the default value |
sig.lev |
sets the level of significance for drawing the bootstrap-based threshold |
max.q |
the maximum number of factors, if |
q.seq |
a vector of factor number candidates; if |
qlen |
specifies the length of the sequence of factor number candidates |
qby |
specifies the increment of the sequence of factor number candidates when |
dw |
trims off the interval of consideration in the binary segmentation algorithm
and determines the minimum length of a stationary segment;
if |
p |
inverse of the average length of blocks in the stationary bootstrap procedure,
if |
B |
the number of bootstrap samples for threshold selection |
scales |
the number of wavelet scales used for wavelet filtering of the common and idiosyncratic components estimated via PCA |
rule |
the depth of a binary tree for change-point analysis, see the Appendix of Barigozzi, Cho & Fryzlewicz (2016) |
mby |
see |
tby |
see |
idio.diag |
if |
do.parallel |
if |
no.proc |
sets the number of processes to be spawned when |
cs.list |
a list of objects returned by an internal function |
r |
the factor number selected from performing change-point analysis on the common component |
common.est.cps |
change-points detected from the common component estimated with |
idio.seg.res |
an object returned by an internal function |
idio.est.cps |
change-points detected from the idiosyncratic component |
gfm |
factor structure of |
q.seq |
a vector containing the range of factor number candidates |
Haeran Cho
J. Bai and S. Ng (2002) Determining the number of factors in approximate factor models. Econometrica. 70: 191-221.
M. Barigozzi, H. Cho and P. Fryzlewicz (2016) Simultaneous multiple change-point and factor analysis for high-dimensional time series, Preprint.
H. Cho (2016) Change-point detection in panel data via double CUSUM statistic. Electronic Journal of Statistics. 10: 2000-2038.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | n <- 50; T <- 200
e <- matrix(rnorm(n*T), nrow=n) # idiosyncratic components
r <- 3 # factor number
Lam <- matrix(rnorm(n*r, 1, 1), nrow=n) # loadings
f <- matrix(rnorm(r*T), nrow=r) # factors
chi <- e*0 # common component
chp <- T/2 # change-point
chi[, 1:chp] <- Lam%*%f[, 1:chp]
Lam <- Lam + matrix(rnorm(n*r, 0, sqrt(2)), nrow=n) # new loadings
chi[, (chp+1):T] <- Lam%*%f[, (chp+1):T]
x <- chi + sqrt(r)*e
fsa <- factor.seg.alg(x, idio.diag=TRUE)
fsa$common.est.cps
fsa$idio.est.cps
fsa$q.seq
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