An estimation method for the seemingly unrelated regression model with contemporaneous covariances based on an efficient recursive algorithm
作者:
William M. Bolstad,
期刊:
Communications in Statistics - Simulation and Computation
(Taylor Available online 1987)
卷期:
Volume 16,
issue 3
页码: 689-698
ISSN:0361-0918
年代: 1987
DOI:10.1080/03610918708812613
出版商: Marcel Dekker, Inc.
关键词: Seemingly unrelated regressions;Kalman filtering algorithm;state vector;state vector estimator
数据来源: Taylor
摘要:
This paper develops an iterative estimation procedure for estimating both the parameters and covariances of the seemingly unrelated regression model with contemporaneous covariances. The method is based on the use of an efficient recursive estimation algorithm for the SUR model with known contemporaneous covariances. The components of each observation vector are used one at a time to simultaneously estimate the parameters using a Bayesian “state vector” approach. This is computationally efficient, since the only matrix inversion required is one by one. The covariances are estimated using the residuals from the fitted model. The procedure iterates between estimating the parameters and estimating the covariances until convergence. The algorithm yields the parameter estimator and an estimator for its distribution, thus providing a sound basis for inferential procedures on the parameters.
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