Abstract

We develop a constrained indirect inference approach based on a wrapped skewed-t auxiliary model for the estimation of the wrapped stable distribution. To improve the finite-sample properties of the estimators, we devise a bootstrap-based estimate of the weighting matrix employed in the indirect inference program. The simulation study suggests that, in terms of root-mean-squared-error, the indirect inference estimator of the skewness parameter is slightly better than the corresponding maximum likelihood estimator, whereas maximum likelihood is mostly preferable for the other parameters. In terms of computing time, maximum likelihood is faster.

中文翻译：

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通过间接推理估计包裹的稳定分布

摘要

我们开发了一种基于包裹偏斜辅助模型的约束间接推理方法，用于估计包裹稳定分布。为了改善估计器的有限样本特性，我们设计了一种基于引导程序的间接推理程序中使用的加权矩阵的估计。模拟研究表明，就均方根误差而言，偏度参数的间接推断估计量略好于相应的最大似然估计量，而最大似然对其他参数更为可取。在计算时间方面，最大似然更快。