General Model-Based Filters for Extracting Cycles and Trends in Economic Time Series
提出一类基于模型的低通和带通滤波器，用于从经济时间序列中提取趋势和周期，通过卡尔曼滤波计算有限样本结果，并应用于美国投资和GDP数据，得到清晰定义的周期。
A class of model-based filters for extracting trends and cycles in economic time series is presented. These lowpass and bandpass filters are derived in a mutually consistent manner as the joint solution to a signal extraction problem in an unobserved-components model. The resulting trends and cycles are computed in finite samples using the Kalman filter and associated smoother. The filters form a class which is a generalization of the class of Butterworth filters, widely used in engineering. They are very flexible and have the important property of allowing relatively smooth cycles to be extracted from economic time series. Perfectly sharp, or ideal, bandpass filters emerge as a limiting case. Applying the method to quarterly series on U.S. investment and GDP shows a clearly defined cycle. © 2003 President and Fellows of Harvard College and the Massachusetts Institute of Technology.