未观测成分模型中的分数趋势
Fractional trends in unobserved components models
摘要：
我们开发了一个泛化的未观察到的组件模型，允许广泛的长期动态模拟的永久组件作为一个分数整合的过程。该模型不需要平稳性，可以转换为状态空间形式。在多元设置中，分数趋势可以产生协整系统。我们推导出卡尔曼滤波估计的共同分数积分分量，并建立一致性和渐近（混合）的最大似然估计的正态性。我们应用该模型提取一个共同的长期组成部分，美国的三个通货膨胀的措施，在那里我们表明，$I(1)$假设是可能违反的共同趋势。
英文摘要：
We develop a generalization of unobserved components models that allows for a wide range of long-run dynamics by modelling the permanent component as a fractionally integrated process. The model does not require stationarity and can be cast in state space form. In a multivariate setup, fractional trends may yield a cointegrated system. We derive the Kalman filter estimator for the common fractionally integrated component and establish consistency and asymptotic (mixed) normality of the maximum likelihood estimator. We apply the model to extract a common long-run component of three US inflation measures, where we show that the $I(1)$ assumption is likely to be violated for the common trend.
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