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Statistics > Methodology
Title: Stable Reduced-Rank VAR Identification
(Submitted on 1 Mar 2024 (v1), last revised 25 Mar 2024 (this version, v2))
Abstract: The vector autoregression (VAR) has been widely used in system identification, econometrics, natural science, and many other areas. However, when the state dimension becomes large the parameter dimension explodes. So rank reduced modelling is attractive and is well developed. But a fundamental requirement in almost all applications is stability of the fitted model. And this has not been addressed in the rank reduced case. Here, we develop, for the first time, a closed-form formula for an estimator of a rank reduced transition matrix which is guaranteed to be stable. We show that our estimator is consistent and asymptotically statistically efficient and illustrate it in comparative simulations.
Submission history
From: Xinhui Rong [view email][v1] Fri, 1 Mar 2024 02:37:11 GMT (560kb)
[v2] Mon, 25 Mar 2024 01:14:56 GMT (1328kb)
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