IARkalman: Maximum Likelihood Estimation of the IAR Model via Kalman...

In iAR: Irregularly Observed Autoregressive Models

Maximum Likelihood Estimation of the IAR Model via Kalman Recursions

Description

Maximum Likelihood Estimation of the IAR model parameter phi. The estimation procedure uses the Kalman Filter to find the maximum of the likelihood.

Usage

IARkalman(y, st, delta = 0, zero.mean = TRUE, standardized = TRUE)

Arguments

y	Array with the time series observations.
st	Array with the irregular observational times.
delta	Array with the measurements error standard deviations.
zero.mean	logical; if TRUE, the array y has zero mean; if FALSE, y has a mean different from zero.
standardized	logical; if TRUE, the array y is standardized; if FALSE, y contains the raw time series.

Value

A list with the following components:

• phi MLE of the phi parameter of the IAR model.

• ll Value of the negative log likelihood evaluated in phi.

References

\insertRef

Eyheramendy_2018iAR

See Also

gentime, IARsample, arima,IARphikalman

Examples

set.seed(6714)
st<-gentime(n=100)
y<-IARsample(phi=0.99,st=st,n=100)
y<-y$series
phi=IARkalman(y=y,st=st)$phi
print(phi)

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