Approximate estimator for parameters of non-normal VMA(1) model

Al-talib, Mehasin Salah; Saaed, Haeifa Abdal Jawad; Salah, Wasfi Taher

doi:10.33899/iqjoss.2019.0164147

	Approximate estimator for parameters of non-normal VMA(1) model
IRAQI JOURNAL OF STATISTICAL SCIENCES
Article 1, Volume 16, Issue 3, 2019, Pages 1-24 PDF (1005.02 K)
DOI: 10.33899/iqjoss.2019.0164147
Authors
Mehasin Salah Al-talib^* ¹; Haeifa Abdal jawad saaed²; Wasfi Taher Salah³
¹Department of Statistics ,College of computer and mathematical seience
²statistic and information, university of mosul
³chic university
Abstract
In many applications of time series , the white noise does not follow the normal distribution but follows one of the heavy tailed distributions. When using normal models in estimation and forecasting, these phenomena they will produce far from reality and inefficient estimators and predictions. The generalized multivariate modified Bessel distribution belongs to the potentially heavy-tailed distribution family and has wide applications in events that change over time. On this basis, this paper concerned with the study of vector moving average model of the first order (VMA(1)), is the white noise error term of this model follows GMMB. The non linear VMA(1) model was approximated to a linear model. The parameters of approximated model was estimated by Bayesian technique when non-informative priors. We supposed that some parameters of the distribution ( ) known. Different loss functions has been used in Bayesian analysis, We proposed two positive weight functions in weighted balanced loss functions . Some of theoretical results were applied on empirical sample generated from VMA(1) model. It is concluded that the estimators under proposed weighted balanced loss functions are better.

Statistics Article View: 465 PDF Download: 530