Dependability of Unstructured Estimator in Vector Autoregression Identification

Abstract

This paper discusses the dependability of the maximum like-lihood estimator (MLE) when the dynamical model is specified as vector autoregression (VAR). When the size of the data vector in VAR is enlarged a little, the distributions of the estimates by the MLE become too wide to satisfy the precision requirement. Consequently, it is necessary to largely increase the length of the tested data for sharpening the distributions and obtaining the suitable estimates. In this paper, we give an explanation of this phenomenon and analyze the convergence relation of each parameter.