Abstract
Let $\mathbf{R}$ be an infinite dimensional stationary covariance matrix, let $\mathbf{R}(k)$ and $\mathbf{W}(k)$ denote the top $k \times k$ left hand corners of $\mathbf{R}$ and $\mathbf{R}^{-1}$ respectively and let $\mathbf{\Sigma}(k)$ and $\mathbf{\Gamma}(k)$ denote the approximations for $\mathbf{R}(k)^{-1}$ suggested by Whittle (1951) and Shaman (1976) respectively. We consider quadratic forms of the type $Q(k) = \beta(k)' \mathbf{R}(k)^{-1}\alpha (k)$, when the vectors $\beta(k)$ and $\alpha(k)$ constitute the first $k$ elements of the infinite absolutely summable sequences $\{\beta_j\}$ and $\{\alpha_j\}$. If $\chi_1(k) = \beta (k)' \mathbf{W}(k) \mathbf{\alpha}(k)$ and $\chi_2(k) = \beta (k)' \mathbf{\Sigma(k)}\mathbf{\alpha}(k)$, then, as $k \rightarrow \infty, Q(k)$ and $\chi_1(k)$ converge to the same limiting value for all such $\alpha (k)$ and $\beta(k)$, but $\chi_2(k)$ does not necessarily do so. Further, if $\tilde\mathbf{\alpha}(k) = (\alpha_k, \cdots, \alpha_1)'$ and $\tilde\mathbf{\beta}(k) = (\beta_k, \cdots, \beta_1)'$ then $\chi_1(k) \equiv \tilde\mathbf{\beta}(k)'\mathbf{\Gamma}(k)\tilde\mathbf{\alpha}(k)$. We discuss the use of $\mathbf{W}(k)$ for evaluating the asymptotic covariance structure of the autoregressive estimates of the inverse covariance function and the moving average parameters.
Citation
R. J. Bhansali. "The Evaluation of Certain Quadratic Forms Occurring in Autoregressive Model Fitting." Ann. Statist. 10 (1) 121 - 131, March, 1982. https://doi.org/10.1214/aos/1176345695
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