by Readers of this blog might have noticed that I prefer to detrend time series using a moving average – and not the advanced and routinely and widely used Hodrick-Prescott filter. Part of this was lazyness. But I also do not like the HP filter: what is it? Why does it wag its tails so much? What is the ‘right’ smoothing parameter? James D. Hamilton has answered my questions (while implicating that loads of research is at least suspect if not worthless): Why You should never use the Hodrick-Prescott filter: “Here’s why. (1) The HP filter produces series with spurious dynamic relations that have no basis in the underlying data-generating process. (2) Filtered values at the end of the sample are very different from those in the middle, and are also characterized by spurious dynamics. (3) A
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Readers of this blog might have noticed that I prefer to detrend time series using a moving average – and not the advanced and routinely and widely used Hodrick-Prescott filter. Part of this was lazyness. But I also do not like the HP filter: what is it? Why does it wag its tails so much? What is the ‘right’ smoothing parameter? James D. Hamilton has answered my questions (while implicating that loads of research is at least suspect if not worthless):
Why You should never use the Hodrick-Prescott filter: “Here’s why. (1) The HP filter produces series with spurious dynamic relations that have no basis in the underlying data-generating process. (2) Filtered values at the end of the sample are very different from those in the middle, and are also characterized by spurious dynamics. (3) A statistical formalization of the problem typically produces values for the smoothing parameter vastly at odds with common practice, e.g., a value for λ far below 1600 for quarterly data. (4) There’s a better alternative. A regression of the variable at date t+h on the four most recent values as of date t offers a robust approach to detrending that achieves all the objectives sought by users of the HP filter with none of its drawbacks.”,