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Estimating the dimension of a linear system

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Author Info
Hannan, E. J.
Abstract

The problem considered is that of estimating the integer or integers that prescribe the dimension of a linear system. These could be the Kronecker indices. Though attention is concentrated on the order or McMillan degree, which specifies the dimension of a minimal state vector, the same results are available for other cases. A fairly complete theorem is proved relating to conditions under which strong or weak convergence will hold for an estimate of the McMillan degree when the estimation is based on minimisation of a criterion of the form log det([Omega]n) + nC(T)/T, where [Omega]n, is the estimate of the prediction error covariance matrix and the McMillan degree is assumed to be n. The conditions relate to the prescribed sequence C(T).

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Article provided by Elsevier in its journal Journal of Multivariate Analysis.

Volume (Year): 11 (1981)
Issue (Month): 4 (December)
Pages: 459-473
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Handle: RePEc:eee:jmvana:v:11:y:1981:i:4:p:459-473

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Keywords: Ergodic stationary process ARMA McMillan degree Kronecker indices manifold AIC strong convergence weak convergence law of the iterated logarithm;

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  1. Perez, Marcos & Ahn, Seung Chan, 2007. "GMM Estimation of the Number of Latent Factors," MPRA Paper 4862, University Library of Munich, Germany. [Downloadable!]
  2. Peter C.B. Phillips & Werner Ploberger, 1992. "Posterior Odds Testing for a Unit Root with Data-Based Model Selection," Cowles Foundation Discussion Papers 1017, Cowles Foundation, Yale University. [Downloadable!]
    Other versions:
  3. Helmut Luetkepohl, 2004. "Forecasting with VARMA Models," Economics Working Papers ECO2004/25, European University Institute. [Downloadable!]
    Other versions:
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