Subset regression time series and its modeling procedures
Consider the linear regression model y(n) = x1(n)[theta]1 + ... + xk(n)[theta]k + w(n) with w(n) assumed a linear time series, especially an ARMA series. Procedures which use recursions only are suggested to identify the non-zero [theta]k and the order of ARMA or subset ARMA residuals. The consistency of these procedures is proved. The convergence rate of LS estimation of regression parameters under these assumption is also discussed. Simulations show good results.
Year of publication: |
1989
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Authors: | Chen, Zhao-Guo ; Ni, Jun-Yuan |
Published in: |
Journal of Multivariate Analysis. - Elsevier, ISSN 0047-259X. - Vol. 31.1989, 2, p. 266-288
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Publisher: |
Elsevier |
Keywords: | subset regression LS estimation linear series ARMA series AIC BIC recursion sweeping algorithm consostency the law of the iterated logarithm |
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