markov {timsac}R Documentation

Maximum Likelihood Computation of Markovian Model

Description

Compute maximum likelihood estimates of Markovian model.

Usage

markov(y, tmp.file=NULL)

Arguments

y a multivariate time series.
tmp.file temporary file name. If NULL (default) output no file.

Details

This function is usually used with "simcon".

Value

id id[i]=1 means that the i-th row of F contains free parameters.
ir ir[i] denotes the position of the last non-zero element within the i-th row of F.
ij ij[i] denotes the position of the i-th non-trivial row within F.
ik ik[i] denotes the number of free parameters within the i-th non-trivial row of F.
grad gradient vector.
matFi initial estimate of the transition matrix (F).
matF transition matrix (F).
matG input matrix (G).
davvar DAVIDON variance.
arcoef AR coefficient matrices. arcoef[i,j,k] shows the value of i-th row, j-th column, k-th order.
impuls impulse response matrices.
macoef MA coefficient matrices. macoef[i,j,k] shows the value of i-th row, j-th column, k-th order.
v inovation 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),1000,2)
  ma <- array(0,dim=c(2,2,2))
  ma[,,1] <- matrix(c( -1.0,  0.0,
                        0.0, -1.0), 2,2,byrow=TRUE)
  ma[,,2] <- matrix(c( -0.2,  0.0,
                       -0.1, -0.3), 2,2,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), 2,2,byrow=TRUE)
  ar[,,2] <- matrix(c( -0.5, -0.2,
                       -0.2, -0.5), 2,2,byrow=TRUE)
  ar[,,3] <- matrix(c( -0.3, -0.05,
                       -0.1, -0.30), 2,2,byrow=TRUE)
  z <- mfilter(y,ar,"recursive")
  markov(z)

[Package timsac version 1.2.1 Index]