BOOSTING: WHY YOU CAN USE THE HP FILTER

Peter C.B. Phillips · Zhentao Shi

110 citations0/0 files storedInternational Economic Review

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Monetary Policy and Economic Impact Forecasting Techniques and Applications Statistical and numerical algorithms

Abstract

Abstract We propose a procedure of iterating the HP filter to produce a smarter smoothing device, called the boosted HP (bHP) filter, based onL2‐boosting in machine learning. Limit theory shows that the bHP filter asymptotically recovers trend mechanisms that involve integrated processes, deterministic drifts, and structural breaks, covering the most common trends that appear in current modeling methodology. A stopping criterion automates the algorithm, giving a data‐determined method for data‐rich environments. The methodology is illustrated in simulations and with three real data examples that highlight the differences between simple HP filtering, the bHP filter, and an alternative autoregressive approach.

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References

1988Testing for a Unit Root in Time Series Regression 1997Postwar U.S. Business Cycles: An Empirical Investigation 1999Measuring Business Cycles: Approximate Band-Pass Filters for Economic Time Series 1995A desicion-theoretic generalization of on-line learning and an application to boosting 2002On Adjusting the Hodrick-Prescott Filter for the Frequency of Observations 1999The Band Pass Filter 1986Understanding spurious regressions in econometrics 2007Emerging Market Business Cycles: The Cycle Is the Trend

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