可能错误指定的MA（1）模型的递归估计：一般算法的收敛性
Recursive estimation of possibly misspecified MA(1) models: Convergence of a general algorithm
摘要：
本文给出了一种递推算法，它具有一般形式，可方便地估计一阶滑动平均模型的系数，并得到了正确和错误设定的MA（1）模型的收敛结果。该算法包括伪线性回归（PLR--也称为AML和$RML_1$）和递归最大似然法（$RML_2$），无需监视。受Hannan（1980）方法的启发，我们的收敛性结果是间接地通过证明递归序列可以用一个满足较简单（Robbins-Monro）形式的递归的序列来近似而得到的，最近已经得到了适用于我们情况的收敛性结果。
英文摘要：
We introduce a recursive algorithm of conveniently general form for estimating the coefficient of a moving average model of order one and obtain convergence results for both correct and misspecified MA(1) models. The algorithm encompasses Pseudolinear Regression (PLR--also referred to as AML and $RML_1$) and Recursive Maximum Likelihood ($RML_2$) without monitoring. Stimulated by the approach of Hannan (1980), our convergence results are obtained indirectly by showing that the recursive sequence can be approximated by a sequence satisfying a recursion of simpler (Robbins-Monro) form for which convergence results applicable to our situation have recently been obtained.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
