markov {timsac} | R Documentation |
Maximum Likelihood Computation of Markovian Model
Description
Compute maximum likelihood estimates of Markovian model.
Usage
markov(y)
Arguments
y |
a multivariate time series. |
Details
This function is usually used with simcon
.
Value
id |
|
ir |
|
ij |
|
ik |
|
grad |
gradient vector. |
matFi |
initial estimate of the transition matrix |
matF |
transition matrix |
matG |
input matrix |
davvar |
DAVIDON variance. |
arcoef |
AR coefficient matrices. |
impulse |
impulse response matrices. |
macoef |
MA coefficient matrices. |
v |
innovation variance. |
aic |
AIC. |
References
H.Akaike, E.Arahata and T.Ozaki (1975) Computer Science Monograph, No.5, Timsac74, A Time Series Analysis and Control Program Package (1). The Institute of Statistical Mathematics.
Examples
x <- matrix(rnorm(1000*2), nrow = 1000, ncol = 2)
ma <- array(0, dim = c(2,2,2))
ma[, , 1] <- matrix(c( -1.0, 0.0,
0.0, -1.0), nrow = 2, ncol = 2, byrow = TRUE)
ma[, , 2] <- matrix(c( -0.2, 0.0,
-0.1, -0.3), nrow = 2, ncol = 2, byrow = TRUE)
y <- mfilter(x, ma, "convolution")
ar <- array(0, dim = c(2,2,3))
ar[, , 1] <- matrix(c( -1.0, 0.0,
0.0, -1.0), nrow = 2, ncol = 2, byrow = TRUE)
ar[, , 2] <- matrix(c( -0.5, -0.2,
-0.2, -0.5), nrow = 2, ncol = 2, byrow = TRUE)
ar[, , 3] <- matrix(c( -0.3, -0.05,
-0.1, -0.30), nrow = 2, ncol = 2, byrow = TRUE)
z <- mfilter(y, ar, "recursive")
markov(z)