Martingale unobserved component models
Martingale unobserved component models
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Martingale 未观测组件模型
DOI：
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发表时间：
2013
期刊：
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通讯作者：
N. Shephard
中科院分区：
文献类型：
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作者：
N. Shephard
我讨论的模型，使当地的水平模型，合理化指数加权移动平均线，有一个随时间变化的信号/噪声比。我称之为鞅分量模型。这使得数据的贴现率局部化。我展示了如何有效地处理这些模型使用辅助粒子滤波器部署M卡尔曼滤波器并行运行相互竞争。这里我们认为M等于1,000或更多。将该模型应用于通货膨胀预测。该模型推广到不可观测的组件模型，其中高斯冲击由鞅差序列取代。
I discuss models which allow the local level model, which rationalised exponentially weighted moving averages, to have a time-varying signal/noise ratio. I call this a martingale component model. This makes the rate of discounting of data local. I show how to handle such models effectively using an auxiliary particle filter which deploys M Kalman filters run in parallel competing against one another. Here one thinks of M as being 1,000 or more. The model is applied to inflation forecasting. The model generalises to unobserved component models where Gaussian shocks are replaced by martingale difference sequences.