论文标题
混合因果关系自回归模型的光谱估计
Spectral estimation for mixed causal-noncausal autoregressive models
论文作者
论文摘要
本文研究了由非高斯误差序列驱动的估计和识别因果，非因果关系自动回归模型的新方法。我们不假定创新的任何参数分布函数。取而代之的是，我们使用高阶累积物的信息，将光谱和双光谱结合在最小距离估计中。我们通过在估计中选择适当的初始值来阐明如何规避参数的非线性和非因果和混合模型中的多模式。此外，我们使用基于估计函数的全局最小值的简单比较标准提出了一种识别方法。通过蒙特卡洛研究，我们发现无偏见的估计参数和正确的识别，因为数据偏离了正态性。我们提出了对八个每月商品价格的经验应用，发现非因果关系和混合因果关系。
This paper investigates new ways of estimating and identifying causal, noncausal, and mixed causal-noncausal autoregressive models driven by a non-Gaussian error sequence. We do not assume any parametric distribution function for the innovations. Instead, we use the information of higher-order cumulants, combining the spectrum and the bispectrum in a minimum distance estimation. We show how to circumvent the nonlinearity of the parameters and the multimodality in the noncausal and mixed models by selecting the appropriate initial values in the estimation. In addition, we propose a method of identification using a simple comparison criterion based on the global minimum of the estimation function. By means of a Monte Carlo study, we find unbiased estimated parameters and a correct identification as the data depart from normality. We propose an empirical application on eight monthly commodity prices, finding noncausal and mixed causal-noncausal dynamics.
