blomar {timsac}R Documentation

Bayesian Method of Locally Stationary Multivariate AR Model Fitting

Description

Locally fit multivariate autoregressive models to non-stationary time series by a Bayesian procedure.

Usage

  blomar(y, max.order=NULL, span)

Arguments

y A multivariate time series.
max.order upper limit of the order of AR model. Default is 2*sqrt(n), where n is the length of the time series y.
span length of basic local span.

Details

The basic AR model is given by

y(t) = A(1)y(t-1) + A(2)y(t-2) +...+ A(p)y(t-p) + u(t),

where p is order of the AR model and u(t) is innovation variance v.

Value

mean mean.
var variance.
bweight Bayesian weight.
aic AIC with respect to the present data.
arcoef AR coefficients. arcoef[[m]][i,j,k] shows the value of i-th row, j-th column, k-th order of m-th model.
v innovation variance.
eaic equivalent AIC of Bayesian model.
init start point of the data fitted to the current model.
end end point of the data fitted to the current model.

References

G.Kitagawa and H.Akaike (1978) A Procedure for the Modeling of Non-stationary Time Series. Ann. Inst. Statist. Math., 30, B, 351–363.

H.Akaike (1978) A Bayesian Extension of The Minimum AIC Procedure of Autoregressive Model Fitting. Research Memo. NO.126. The institute of Statistical Mathematics.

H.Akaike, G.Kitagawa, E.Arahata and F.Tada (1979) Computer Science Monograph, No.11, Timsac78. The Institute of Statistical Mathematics.

Examples

  data(Amerikamaru)
  blomar(Amerikamaru, max.order=10, span=300)

[Package timsac version 1.2.1 Index]