BIARforecast: Forecast from BIAR model

In iAR: Irregularly Observed Autoregressive Models

Forecast from BIAR model

Description

Forecast from models fitted by BIARkalman

Usage

BIARforecast(phiR, phiI, y1, y2, t, tAhead)

Arguments

phiR	Autocorrelation coefficient of BIAR model.
phiI	Cross-correlation coefficient of BIAR model.
y1	Array with the observations of the first time series of the BIAR process.
y2	Array with the observations of the second time series of the BIAR process.
t	Array with the observational times.
tAhead	The time ahead for which the forecast is required.

Value

A list with the following components:

• fitted Fitted values by the BIAR model.

• forecast Point forecast in the time ahead required.

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

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

References

\insertRef

Elorrieta_2021iAR

See Also

BIARsample, BIARkalman, BIARfit

Examples

#Simulated Data
n=100
set.seed(6714)
st<-gentime(n)
x=BIARsample(n=n,phiR=0.9,phiI=0.3,st=st)
biar=iAR::BIARkalman(y1=x$y[1,],y2=x$y[2,],t=st)
forBIAR<-BIARforecast(phiR=biar$phiR,phiI=biar$phiI,y1=x$y[1,],y2=x$y[2,],t=st,tAhead=c(1.3))

iAR documentation built on Nov. 25, 2022, 1:06 a.m.