Inference with the Whittle Likelihood: A Tractable Approach Using Estimating Functions
针对Whittle似然估计渐近分布依赖高阶谱难以计算的问题，本文将其嵌入估计函数框架，实现渐近分布的实证估计，并修正了参数置信区间计算中的不准确性。
The theoretical properties of the Whittle likelihood have been studied extensively for many different types of process. In applications however, the utility of the approach is limited by the fact that the asymptotic sampling distribution of the estimator typically depends on third‐order and fourth‐order properties of the process that may be difficult to obtain. In this article, we show how the methodology can be embedded in the standard framework of estimating functions, which allows the asymptotic distribution to be estimated empirically without calculating higher‐order spectra. We also demonstrate that some aspects of the inference, such as the calculation of confidence regions for the entire parameter vector, can be inaccurate but that a small adjustment, designed for application in situations where a mis‐specified likelihood is used for inference, can lead to marked improvements.