Bootstrapping Whittle estimators
提出一种频域自助法来估计Whittle估计量的分布，允许模型设定错误和弱依赖条件，适用于多种改进的Whittle估计量，并通过模拟和太阳黑子数据分析验证了方法的有效性。
Summary Fitting parametric models by optimizing frequency-domain objective functions is an attractive approach of parameter estimation in time series analysis. Whittle estimators are a prominent example in this context. Under weak conditions and the assumption that the true spectral density of the underlying process does not necessarily belong to the parametric class of spectral densities fitted, the distribution of Whittle estimators typically depends on difficult to estimate characteristics of the underlying process. This makes the implementation of asymptotic results for the construction of confidence intervals or for assessing the variability of estimators difficult in practice. In this paper we propose a frequency-domain bootstrap method to estimate the distribution of Whittle estimators that is asymptotically valid under assumptions that not only allow for possible model misspecification, but also for weak dependence conditions that are satisfied by a wide range of stationary stochastic processes. Adaptations of the bootstrap procedure developed to incorporate different modifications of Whittle estimators proposed in the literature, such as, for instance, tapered, debiased or boundary extended Whittle estimators, are also considered. Simulations demonstrate the capabilities of the bootstrap method proposed and its good finite sample performance. A real-life data analysis on sunspots is also presented.