Stepwise least-squares estimation of a multivariate AR(p) model based on the algorithm of Neumaier and Schneider (2001).
mAr.est(x, p, ...)
matrix of multivariate time series
model order
additional arguments for specific methods
A list with components:
Schwartz Bayesian Criterion
vector of intercept terms
matrix of estimated autoregression coefficients for the fitted model
noise covariance matrix
residuals from the fitted model
Fits by stepwise least squares an m-variate AR(p) model given by $$X[t]=w + A1 X[t-1] +...+ Ap X[t-p] +e[t]$$ where X[t]=[X1(t)...Xm(t)]' is a vector of length m w is a m-length vector of intercept terms A=[A1 ... Ap] is a mp x m matrix of autoregressive coefficients e(t) is a m-length uncorrelated noise vector with mean 0 and m x m covariance matrix C
Barbosa S.M., Silva M.E., Fernandes M.J. (2006), Multivariate autoregressive modelling of sea level time series from TOPEX/Poseidon satellite altimetry. Nonlinear Processes in Geophysics, 13, 177-184.
Neumaier, A. and Schneider, T. (2001), Estimation of parameters and eigenmodes of multivariate autoregressive models. ACM Transactions on Mathematical Software, 27, 1, 27-57. Schneider, T. and Neumaier, A. (2001), A Matlab package fo the estimation of parameters and eigenmodes of multivariate autoregressive models, 27, 1, 58-65. Lutkepohl, H. (1993), Introduction to Multiple Time Series Analysis. Springer-Verlag, Berlin.
# NOT RUN {
data(pinkham)
y=mAr.est(pinkham,2,5)
# }
Run the code above in your browser using DataLab