mlocar {timsac} | R Documentation |
Locally fit autoregressive models to non-stationary time series by minimum AIC procedure.
mlocar(y, max.order=NULL, span, const=0, plot=TRUE)
y |
a univariate 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 the basic local span. |
const |
integer. 0 denotes constant vector is not included as a regressor and 1 denotes constant vector is included as the first regressor. |
plot |
logical. If TRUE (default) spectrums pspec are plotted. |
The data of length n are devided into k locally stationary spans,
|<-- n1 -->|<-- n2 -->|<-- n3 -->|.........|<-- nk -->|
where ni (i=1,...,k)
denotes the number of basic spans, each of length span,
which constitute the i-th locally stationary span. At each local span,
the process is represented by a stationary autoregressive model.
mean |
mean. |
var |
variance. |
ns |
the number of local spans. |
order |
order of the current model. |
arcoef |
AR coefficients of current model. |
v |
innovation variance of the current model. |
init |
initial point of the data fitted to the current model. |
end |
end point of the data fitted to the current model. |
pspec |
power spectrum. |
npre |
data length of the preceding stationary block. |
nnew |
data length of the new block. |
order.mov |
order of the moving model. |
v.mov |
innovation variance of the moving model. |
aic.mov |
AIC of the moving model. |
order.const |
order of the constant model. |
v.const |
innovation variance of the constant model. |
aic.const |
AIC of the constant model. |
G.Kitagawa and H.Akaike (1978) A Procedure for The Modeling of Non-Staionary Time Series. Ann. Inst. Statist. Math., 30, B, 351–363.
H.Akaike, G.Kitagawa, E.Arahata and F.Tada (1979) Computer Science Monograph, No.11, Timsac78. The Institute of Statistical Mathematics.
data(locarData) z <- mlocar(locarData, max.order=10, span=300, const=0) z$arcoef