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Demonstrates improved trend estimation in economic data, suggesting better analytical clarity.
The most widely used method for trend estimation in economics is the Hodrick-Prescott (HP) filter. The HP filter has various disadvantages, such as the arbitrary, frequency-dependent choice of the smoothing parameter λ, boundary problems, and difficult interpretation when linking to economic theory. We suggest an alternative method by improving some of these disadvantages using a data-driven, endogenous nonparametric trend estimation. A simulation study and different applications demonstrate the advantages of the nonparametric trend compared to the HP filter. We identify optimal time windows supporting the momentary growth trend. Within this window, economic fundamentals smoothly change and drive the trend.
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Fritz et al. (2026) studied this question.
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