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GENERATION OF MULTIVARIATE AUTOREGRESSIVE SEQUENCES WITH EMPHASIS ON INITIAL VALUES
Date
1992-12-01
Author
ULA, TA
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Certain aspects of data generation are studied through multivariate autoregressive (AR) models. The main emphasis is on the preservation of certain desired moments and the effect of initial values on these moments. The problem of preservation of moments is approached in a nontraditional way by starting with the initial values. For this purpose, general AR processes with a random start and with time-varying parameters are introduced to lay a foundation for the analysis of all types of AR processes, including the periodic cases. It is shown that an AR process with a random start and with parameters obtained from the moment equations is capable of generating jointly multivariate normal vectors with any specified means and covariance matrices, and with any specified autocovariance matrices up to a given lag. With a random start, there is no transition period involved for achieving these moments. A simple solution is proposed for matrix equations of the form BB(T) = A which appear in the moment equations. The aggregation properties of general AR process are also studied.
Subject Keywords
Water Science and Technology
URI
https://hdl.handle.net/11511/63670
Journal
JOURNAL OF HYDROLOGY
DOI
https://doi.org/10.1016/0022-1694(92)90241-m
Collections
Department of Statistics, Article
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BibTeX
T. ULA, “GENERATION OF MULTIVARIATE AUTOREGRESSIVE SEQUENCES WITH EMPHASIS ON INITIAL VALUES,”
JOURNAL OF HYDROLOGY
, vol. 140, pp. 209–233, 1992, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/63670.