Seasonal Extraction in the Presence of Feedback
研究在货币政策等反馈控制背景下，如何用卡尔曼滤波器从受季节性噪声污染的时间序列中提取季节性成分，指出传统单变量季节性提取方法在此类模型中不适用。
Abstract Time series seasonal extraction techniques are quite often applied in the context of a policy aimed at controlling the nonseasonal components of a time series. Monetary policies targeting the nonseasonal components of monetary aggregates are an example. Such policies can be studied as a quadratic optimal control model in which observations are contaminated by seasonal noise. Optimal extraction filters in such models do not correspond to univariate time series seasonal extraction filters. The linear quadratic control model components are nonorthogonal due to the presence of control feedback. This article presents the Kalman filter as a conceptual and computational device used to extract seasonal noise in the presence of feedback. KEY WORDS: Seasonal decompositionsKalman filterUnobserved component modelsModel-based approachMonetary targeting and control