Asymptotic Expansions of the Distributions of Statistics Related to the Spectral Density Matrix in Multivariate Time Series and Their Applications
研究了多元高斯平稳过程谱密度矩阵估计量的分布渐近展开，并应用于二维AR(1)模型（如蛛网模型简化形式），给出了估计量、相干性和贡献率的二阶渐近分布。
Let { X ( t )} be a multivariate Gaussian stationary process with the spectral density matrix f 0 (ω), where θ is an unknown parameter vector. Using a quasi-maximum likelihood estimator θ̂ of θ, we estimate the spectral density matrix f 0 (ω) by f θ̂ (ω). Then we derive asymptotic expansions of the distributions of functions of f θ̂ (ω). Also asymptotic expansions for the distributions of functions of the eigenvalues of f θ̂ (ω) are given. These results can be applied to many fundamental statistics in multivariate time series analysis. As an example, we take the reduced form of the cobweb model which is expressed as a two-dimensional vector autoregressive process of order 1 (AR(1) process) and show the asymptotic distribution of θ̂ , the estimated coherency, and contribution ratio in the principal component analysis based on θ̂ in the model, up to the second-order terms. Although our general formulas seem very involved, we can show that they are tractable by using REDUCE 3.