Endogenous Nonparametric Trend Estimation for Economic Data - An Enhanced Alternative to the Hodrick-Prescott Filter

The most widely used method for trend estimation in economics is the Ho-drick-Prescott (HP) filter. The HP filter has various disadvantages as the arbitrary and 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 purely data-driven, endog-enous 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.