Order determination in general vector autoregressions
Bent Nielsen
Abstract
In the application of autoregressive models the order of the model is often estimated using either a sequence of likelihood ratio tests, a likelihood based information criterion, or a residual based test. The properties of such procedures has been discussed extensively under the assumption that the characteristic roots of the autoregression are stationary. While non-stationary situations have also been considered the results in the literature depend on conditions to the characteristic roots. It is here shown that these methods for lag length determination can be used regardless of the assumption to the characteristic roots and also in the presence of deterministic terms. The proofs are based on methods developed by C. Z. Wei in his joint work with T. L. Lai.
Create a lesson
Related papers
Instance-Optimal Adaptive Location Estimation via Multiscale Mid-Summaries
Qiaosen Wang, Chao Gao
Robust Multi-Task Learning for Principal Component Analysis
Dali Liu, Haolei Weng
Principal component error in high-dimensional factor models
Alex Bernstein, Lisa R. Goldberg, Nicholas Gunther et al.
Approximation Theorems for High-Dimensional Canonical U-Statistics: Gaussian Chaos and Phase Transition
Leheng Cai, Qirui Hu
On the parametric and semiparametric Fisher information matrix for non-zero mean stationary spherical invariant random processes
Jean-Pierre Delmas, Habti Abeida, Stefano Fortunati
Inference for two-stage sampling in spatial surveys
Guillaume Chauvet, Olivier Bouriaud, Trinh H. K. Duong