Estimating MA Parameters through Factorization of the Autocovariance Matrix and an MA‐Sieve Bootstrap
提出一种基于自协方差矩阵的修正Cholesky分解来估计平稳时间序列移动平均系数的新方法，并用于构建MA筛子自助法，模拟显示效果良好。
A new method to estimate the moving‐average (MA) coefficients of a stationary time series is proposed. The new approach is based on the modified Cholesky factorization of a consistent estimator of the autocovariance matrix. Convergence rates are established, and the new estimates are used to implement an MA‐type sieve bootstrap. Finite‐sample simulations corroborate the good performance of the proposed methodology.