Abstract
Current methods for prediction of singular vector autoregressive (SVAR) processes. re computationally expensive and only approximate. Furthermore they cannot completely characterize the SVAR processes. In this paper we present an LWRS algorithm which estimates a reduced innovation and a generalized generating transfer function, by which the SVAR process can be uniquely characterized and synthesized with probability one. Deterministic relations (DR) in the component spaces of the process are used as a main tool in the investigation of degeneration. A computationally efficient extended LWR algorithm is obtained to estimate the reduced innovation, which is equivalent to the SVAR process according to a new equivalence definition. A complete innovation estimation algorithm is also obtained. These algorithms are extremely simple for most SVAR processes.
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