IDENTIFICATION OF AN AUTOREGRESSIVE MODEL FOR MULTIVARIATE ONE-DIMENSIONAL NONSTATIONARY GAUSSIAN RANDOM PROCESSES

Autoregressive Gaussian Random Vectors of First Order

Model Identification for Infinite Variance Autoregressive Processes

We consider model identification for infinite variance autoregressive time series processes. It is shown that a consistent estimate of autoregressive model order can be obtained by minimizing Akaike’s information criterion, and we use all-pass models to identify noncausal autoregressive processes and estimate the order of noncausality (the number of roots of the autoregressive polynomial inside...

autoregressive gaussian random vectors of first order

A Generalized Convolution Model for Multivariate Nonstationary Spatial Processes

We propose a constructive method for specifying flexible classes of nonstationary stochastic models for multivariate spatial data. The method is based upon convolutions of spatially varying covariance functions and produces mathematically valid covariance structures. This method generalizes the convolution approach suggested by Majumdar and Gelfand (2007) to extend multivariate spatial covarian...

Estimation for Partially Nonstationary Multivariate Autoregressive Models with Conditional Heteroskedasticity

where $‘s inre r*onst,ant matricaes; detI{@(z)} = 11 @,x . w * $JP[ = 0 has ci 5 771, urrit roots and ‘I’ = 711 d roots omside the urrit, circle: tPt = ((1 it, 7 c+> is a sequcnce of independent1 and idcntically distlributled (i.i.tl) matrices with mean zero and nonnegativc covarianc~e IC[ /le+&) ~f’(&)] = 0; pit is an i.i.d ramlom vector witIh mean zero and positive covariance E ( etef j = CA ...