mlomar {timsac} | R Documentation |
Locally fit multivariate autoregressive models to non-stationary time series by the minimum AIC procedure using the householder transformation.
mlomar(y, max.order = NULL, span, const = 0)
y |
a multivariate time series. |
max.order |
upper limit of the order of AR model, less than or equal to
n/2d where n is the length and d is the dimension of the
time series |
span |
length of basic local span. Let m denote |
const |
integer. '0' denotes constant vector is not included as a regressor and '1' denotes constant vector is included as the first regressor. |
The data of length n are divided into k locally stationary spans,
|<-- n1 -->|<-- n2 -->|<-- n3 -->| ..... |<-- nk -->|
where ni (i=1,...,k) denoted 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. |
aic |
AIC of the current model. |
arcoef |
AR coefficient matrices of the 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. |
npre |
data length of the preceding stationary block. |
nnew |
data length of the new block. |
order.mov |
order of the moving model. |
aic.mov |
AIC of the moving model. |
order.const |
order of the constant model. |
aic.const |
AIC of the constant model. |
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, G.Kitagawa, E.Arahata and F.Tada (1979) Computer Science Monograph, No.11, Timsac78. The Institute of Statistical Mathematics.
data(Amerikamaru) mlomar(Amerikamaru, max.order = 10, span = 300, const = 0)