Kolmogorov-Wiener Filters for Finite Time-Series
نویسندگان
چکیده
This paper describes a framework of how to optimally implement linear filters for finite time series. The filters under consideration have the property that they minimize the mean squared error compared to some ideal hypothetical filter. It is shown in examples that three commonly used filters, the bandpass filter, the Hodrick-Prescott filter and the digital Butterworth filter need to be adjusted when applied to finite samples of serially correlated or integrated data. An empirical example indicates that the proposed optimal filters improve the end-of-sample performance of standard filters when applied to U.S. GDP data.
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