blomar: 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.
Value
meanmean.
varvariance.
bweightBayesian weight.
aicAIC with respect to the present data.
arcoefAR coefficients. arcoef[[m]][i,j,k] shows the value of $i$-th row, $j$-th column, $k$-th order of $m$-th model.
vinnovation variance.
eaicequivalent AIC of Bayesian model.
initstart point of the data fitted to the current model.
endend point of the data fitted to the current model.
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.
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.