DocumentCode
2190831
Title
Dependability of Unstructured Estimator in Vector Autoregression Identification
Author
Lu, Xin ; Nishiyama, Kiyoshi
Author_Institution
Department of Computer and Information Sciences, Faculty of Engineering, Iwate University, 4-3-5, Ueda, Morioka, 020-8551, JAPAN, luxin@cis.iwate-u.ac.jp
fYear
2007
fDate
17-19 Oct. 2007
Firstpage
589
Lastpage
594
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.
Keywords
Convergence; Covariance matrix; Economic forecasting; Equations; Humans; Macroeconomics; Maximum likelihood estimation; Predictive models; Reactive power; Testing; maximum likelihood estimator; residual error; vector autoregression;
fLanguage
English
Publisher
ieee
Conference_Titel
Signal Processing Systems, 2007 IEEE Workshop on
Conference_Location
Shanghai, China
ISSN
1520-6130
Print_ISBN
978-1-4244-1222-8
Electronic_ISBN
1520-6130
Type
conf
DOI
10.1109/SIPS.2007.4387615
Filename
4387615
Link To Document