Order selection for vector autoregressive models

Order selection for vector autoregressive models

Order-selection criteria for vector autoregressive (AR) modeling are discussed. The performance of an order-selection criterion is optimal if the model of the selected order is the most accurate model in the considered set of estimated models: here vector AR models. Suboptimal performance can be a result of underfit or overfit. The Akaike information criterion (AIC) is an asymptotically unbiase...

Order selection of autoregressive models

This correspondence addreskes the problem of order determination of autoregressive models by Bayesian predictive densities. A criterion is derived employing noninformative prior densities of the model parameters. The form of the obtained criterion coincides with that of Rissanen in 1161. Simulation results are presented which demonstrate the good performance of the criterion, and comparisons wi...

Vector Autoregressive Models

Directed Graphs and Variable Selection in Large Vector Autoregressive Models

We represent the dynamic relation among variables in vector autoregressive (VAR) models as directed graphs. Based on these graphs, we identify so-called strongly connected components (SCCs). Using this graphical representation, we consider the problem of variable selection. We use the relations among the strongly connected components to select variables that need to be included in a VAR if inte...

Finite sample criteria for autoregressive order selection

The quality of selected AR models depends on the true process in the finite sample practice, on the number of observations, on the estimation algorithm, and on the order selection criterion. Samples are considered to be finite if the maximum candidate model order for selection is greater than 10, where denotes the number of observations. Finite sample formulae give empirical approximations for ...