非线性状态空间模型的反演Copula
Inversion Copulas from Nonlinear State Space Models
摘要：
我们建议从非线性状态空间模型的反演中构造Copula函数。这些允许新的时间序列模型，具有相同的状态空间模型的串行依赖结构，但具有任意的边缘分布，和灵活的密度预测。我们研究的时间序列属性的Copula，概述了串行依赖措施，并估计模型使用基于似然的方法。被认为是从三个例子的状态空间模型构造的Copula：一个随机波动率模型与不可观测的组件，马尔可夫切换自回归，和高斯线性不可观测的组件模型。我们发现，所有三个反演copula与灵活的利润率提高了适合和密度预测的季度美国广义通货膨胀和电力通货膨胀。
英文摘要：
We propose to construct copulas from the inversion of nonlinear state space models. These allow for new time series models that have the same serial dependence structure of a state space model, but with an arbitrary marginal distribution, and flexible density forecasts. We examine the time series properties of the copulas, outline serial dependence measures, and estimate the models using likelihood-based methods. Copulas constructed from three example state space models are considered: a stochastic volatility model with an unobserved component, a Markov switching autoregression, and a Gaussian linear unobserved component model. We show that all three inversion copulas with flexible margins improve the fit and density forecasts of quarterly U.S. broad inflation and electricity inflation.
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