论文标题
与稳定创新的本地固定ARMA过程的间接推断
Indirect inference for locally stationary ARMA processes with stable innovations
论文作者
论文摘要
局部固定过程类别假定存在时间变化的光谱表示，即有限的第二刻。我们通过将创新修改为稳定分布和间接推断以估算这种类型的模型来提出$α$稳定的本地固定过程。由于无限差异，无法定义一些有趣的属性，例如随时间变化的自动相关。但是，由于$α$稳定的分布家族在线性组合下关闭，其中包括处理不对称和较厚的尾巴，因此所提出的模型在整个过程中具有相同的尾巴行为。在本文中，我们提出了这个新模型，呈现过程的理论特性，并进行与间接推理有关的模拟，以估算模型的参数形式。最后，说明了经验应用。
The class of locally stationary processes assumes that there is a time-varying spectral representation, that is, the existence of finite second moment. We propose the $α$-stable locally stationary process by modifying the innovations into stable distributions and the indirect inference to estimate this type of model. Due to the infinite variance, some of interesting properties such as time-varying auto-correlation cannot be defined. However, since the $α$-stable family of distributions is closed under linear combination which includes the possibility of handling asymmetry and thicker tails, the proposed model has the same tail behavior throughout the time. In this paper, we propose this new model, present theoretical properties of the process and carry out simulations related to the indirect inference in order to estimate the parametric form of the model. Finally, an empirical application is illustrated.
