CIARfit {iAR}

Fitted Values of CIAR model

Description

Fit a CIAR model to an irregularly observed time series.

Usage

CIARfit(phiValues, y, t, standardized = TRUE, c = 1)

Arguments

phiValues

An array with the parameters of the CIAR model. The elements of the array are, in order, the real and the imaginary part of the phi parameter of the CIAR model.

y

Array with the time series observations.

t

Array with the irregular observational times.

standardized

logical; if TRUE, the array y is standardized; if FALSE, y contains the raw time series

c

Nuisance parameter corresponding to the variance of the imaginary part.

Value

A list with the following components:

• yhat Fitted values of the observable part of CIAR model.

• xhat Fitted values of both observable part and imaginary part of CIAR model.

• Lambda Lambda value estimated by the CIAR model at the last time point.

• Theta Theta array estimated by the CIAR model at the last time point.

• Sighat Covariance matrix estimated by the CIAR model at the last time point.

• Qt Covariance matrix of the state equation estimated by the CIAR model at the last time point.

References

Elorrieta, F, Eyheramendy, S, Palma, W (2019). “Discrete-time autoregressive model for unequally spaced time-series observations.” A&A, 627, A120. doi: 10.1051/0004-6361/201935560, https://doi.org/10.1051/0004-6361/201935560.

See Also

gentime, CIARsample, CIARphikalman,CIARkalman

Examples

n=100
set.seed(6714)
st<-gentime(n)
x=CIARsample(n=n,phiR=0.9,phiI=0,st=st,c=1)
y=x$y
y1=y/sd(y)
ciar=CIARkalman(y=y1,t=st)
ciar
yhat=CIARfit(phiValues=c(ciar$phiR,ciar$phiI),y=y1,t=st)