Factorization of moving-average spectral densities by state-space representations and stacking.

*(English)*Zbl 1077.62074Summary: To factorize a spectral density matrix of a vector moving average process, we propose a state space representation. Although this state space is not necessarily of minimal dimension, its associated system matrices are simple and most matrix multiplications involved are nothing but index shifting. This greatly reduces the complexity of computation. Moreover, we stack every \(q\) consecutive observations of the original process MA(\(q\)) and generate a vector MA(1) process. We consider a similar state space representation for the stacked process. Consequently, the solution hinges on a surprisingly compact discrete algebraic Riccati equation (DARE), which involves only one Toeplitz and one Hankel block matrix composed of autocovariance functions. One solution to this equation is given by the so-called iterative projection algorithm. Each iteration of the stacked version is equivalent to \(q\) iterations of the unstacked one.

We show that the convergence behavior of the iterative projection algorithm is characterized by the decreasing rate of the partial correlation coefficients for the stacked process. In fact, the calculation of the partial correlation coefficients via the Whittle algorithm, which takes a very simple form in this case, offers another solution to the problem. To achieve computational efficiency, we apply the general Newton procedure given by P. Lancaster and L. Rodman [Algebraic Riccati equations. (1995; Zbl 0836.15005)] to the DARE and obtain an algorithm of quadratic convergence rate. One immediate application of the new algorithms is polynomial stabilization. We also discuss various issues such as check of positivity and numerical implementation.

We show that the convergence behavior of the iterative projection algorithm is characterized by the decreasing rate of the partial correlation coefficients for the stacked process. In fact, the calculation of the partial correlation coefficients via the Whittle algorithm, which takes a very simple form in this case, offers another solution to the problem. To achieve computational efficiency, we apply the general Newton procedure given by P. Lancaster and L. Rodman [Algebraic Riccati equations. (1995; Zbl 0836.15005)] to the DARE and obtain an algorithm of quadratic convergence rate. One immediate application of the new algorithms is polynomial stabilization. We also discuss various issues such as check of positivity and numerical implementation.

##### MSC:

62M15 | Inference from stochastic processes and spectral analysis |

47A68 | Factorization theory (including Wiener-Hopf and spectral factorizations) of linear operators |

65C60 | Computational problems in statistics (MSC2010) |

93B20 | Minimal systems representations |

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\textit{L. M. Li}, J. Multivariate Anal. 96, No. 2, 425--438 (2005; Zbl 1077.62074)

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