This article introduces the class of periodic trawl processes, which are continuous-time, infinitely divisible, stationary stochastic processes, that allow for periodicity and flexible forms of their serial correlation, including both short- and long-memory settings. We derive some of the key probabilistic properties of periodic trawl processes and present relevant examples. Moreover, we show how such processes can be simulated and establish the asymptotic theory for their sample mean and sample autocovariances. Consequently, we prove the asymptotic normality of a (generalised) method-of-moments estimator for the model parameters. We illustrate the new model and estimation methodology in an application to electricity prices.
翻译:本条介绍定期拖网过程的类别,这些过程是连续的、无限分散的、固定的、随机的,允许周期性和灵活的序列关联形式,包括短期和长期的设置,我们从中得出定期拖网过程的一些关键概率特性,并举一些有关的例子,此外,我们展示如何模拟这些过程,为其样本的中值和样本的自动变异性建立无药可治理论,因此,我们证明模型参数的(通用的)移动方法估计器是无药可治的,我们用新的模型和估计方法来说明电价应用。</s>