nonst {timsac}R Documentation

Non-stationary Power Spectrum Analysis

Description

Locally fit autoregressive models to non-stationary time series by AIC criterion.

Usage

nonst(y, span, max.order=NULL, plot=TRUE)

Arguments

y a univariate time series.
span length of the basic local span.
max.order highest order of AR model. Default is 2*sqrt(n), where n is the length of the time series y.
plot logical. If TRUE (the default) spectrums are plotted.

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.

AIC is defined by

AIC = nlog(det(sd)) + 2k

where n is the length of data, sd is the estimates of the innovation variance and k is the number of parameter.

Value

ns the number of local spans.
arcoef AR coefficients.
v innovation variance.
aic AIC.
daic21 = AIC2-AIC1.
daic = daic21/n (n is the length of the time series "y").
init start point of the data fitted to the current model.
end end point of the data fitted to the current model.
pspec power spectrum.

References

H.Akaike, E.Arahata and T.Ozaki (1976) Computer Science Monograph, No.6, Timsac74 A Time Series Analysis and Control Program Package (2). The Institute of Statistical Mathematics.

Examples

# Non-stationary Test Data
  data(nonstData)
  nonst(nonstData, span=700, max.order=49)

[Package timsac version 1.2.1 Index]