cross.cor.MSAR: empirical cross-correlation for multivariate MSAR time series

In NHMSAR: Non-Homogeneous Markov Switching Autoregressive Models

Description

cross.cor.MSAR computes the cross-correlation between two components. The cross-corelation can be estimted for the whole time series or regime by regime.

Usage

cross.cor.MSAR(data, X=NULL, nc1 = 1, nc2 = 2, lag = 10, regime = 0, 
CI = FALSE, Bsim = 0, N.samples = 1, add = FALSE, 
col = 1, names = NULL, alpha = 0.1,ylab="Cross-Correlation", dt = 1, ylim = c(-0.1, 1))

Arguments

data	observed (or reference) time series, array of dimension T*N.samples*d
X	time series of regimes associated to data
nc1	first component to be considered
nc2	second component to be considered
lag	maximum lag (default=10). The cross-correlation is estimated for lags -lag:lag.
regime	has to be an integer between 0 and M, with M the number of regimes. If regime=0, the cross correlaiton is computed for the whole time series. If regime=m>0, the corss corelation is computed considereing only the sub-sequences in regime m.
CI	If CI=TRUE fluctuation intervals are computed, default is FALSE
Bsim	useful for computation of confidence intervals. When observed and simulated data are compared, one expects that the number of simulated time series is Bsim*N.samples
N.samples	useful for computation of confidence intervals. N.sample describes the number of independant time series in the observed (or reference) data
dt	default time step is equal to 1
add	if add=TRUE the empirical cross-correlation is added to the current plot.
col	color of the line
names	list with the names of components of data
alpha	level for the computation of the fluctuation intervals. default=0.1
ylab	legend for y axis
ylim	limit for y axis

Details

The cross-correlation functions are computed from one or several independent realizations of the same length.

Value

returns a list including:

..$ccf	empirical cross-correlation
..$lag	abscissa for the cross-correlation
..$CI	fluctuation intervals

Author(s)

Valerie Monbet, [e-mail]

References

Bessac, J., Ailliot, P., & Monbet, V. (2013). Gaussian linear state-space model for wind fields in the North-East Atlantic. arXiv preprint arXiv:1312.5530.

See Also

cor.MSAR, cor, valid_all

Examples

data(Wind)
T = dim(U)[1]
c = cross.cor.MSAR(U,nc1=1,nc2=18,names=1:18)
## Not run
#Y = U[,,c(1,18)]
#theta.init=init.theta.MSAR(Y,M=2,order=2,label="HH")
#res.hh = fit.MSAR(Y,theta.init,verbose=TRUE,MaxIter=200)
#Bsim = 20
#N.samples = dim(U)[2]
#Ksim = Bsim*N.samples
#Y0 = Y0
#Y.sim = simule.nh.MSAR(res.hh$theta,Y0 = Y0,T,N.samples = Ksim) 
#c.sim = cross.cor.MSAR(Y.sim$Y,nc1=1,nc2=2,names=c(1,18),
#   CI=TRUE,Bsim=Bsim,N.samples=N.samples,add=TRUE,col="red")

NHMSAR documentation built on Feb. 9, 2022, 9:06 a.m.