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首页> 外文期刊>Journal of statistical computation and simulation >Empirical characteristic function tests for GARCH innovation distribution using multipliers
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Empirical characteristic function tests for GARCH innovation distribution using multipliers

机译：使用乘数的GARCH创新分布的经验特征函数检验

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摘要

Goodness-of-fit tests for the innovation distribution in GARCH models based on measuring deviations between the empirical characteristic function of the residuals and the characteristic function under the null hypothesis have been proposed in the literature. The asymptotic distributions of these test statistics depend on unknown quantities, so their null distributions are usually estimated through parametric bootstrap (PB). Although easy to implement, the PB can become very computationally expensive for large sample sizes, which is typically the case in applications of these models. This work proposes to approximate the null distribution through a weighted bootstrap. The procedure is studied both theoretically and numerically. Its asymptotic properties are similar to those of the PB, but, from a computational point of view, it is more efficient.
机译：文献中提出了基于测量残差的经验特征函数与零假设下的特征函数之间的偏差的GARCH模型中创新分布的拟合优度检验。这些测试统计量的渐近分布取决于未知量，因此它们的零分布通常通过参数自举（PB）进行估算。尽管易于实现，但对于大样本量，PB在计算上会变得非常昂贵，在这些模型的应用中通常就是这种情况。这项工作建议通过加权引导程序来近似零分布。对该程序进行了理论和数值研究。它的渐近特性类似于PB的渐近特性，但从计算角度来看，它的效率更高。

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