Confidence ellipsoids for the primary regression coefficients in m- equation seemingly unrelated regression models
Year: 2018
Journal of Information and Computing Science, Vol. 13 (2018), Iss. 4 : pp. 269–282
Abstract
For a m-equation seemingly unrelated regression(SUR) model, this paper derives two basic confidence ellipsoids(CEs) respectively based on the two-stage estimation and maximum likelihood estimation(MLE), and corrects the two CEs using the Bartlett correction method, resulting in four new CEs. In the meantime via using the partition matrix, we derive a new matrix-derivative-based formulation of Fisher's information matrix for calculating the maximum likelihood estimator of the m-equation SUR model. By Monte Carlo simulation, the coverage probabilities and average volumetric characteristics of CEs are compared under different sample values and different correlation coefficients. Moreover, it is proved that the CE based on the second bartlett correction method performs better even in the case of small samples. Finally, we apply these CEs to the actual data for analysis. The CEs of the SUR model with multiple equations are found to be more accurate than the case with only two equations.
Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/2024-JICS-22436
Journal of Information and Computing Science, Vol. 13 (2018), Iss. 4 : pp. 269–282
Published online: 2018-01
AMS Subject Headings:
Copyright: COPYRIGHT: © Global Science Press
Pages: 14