Open Access
2010 The Hodrick-Prescott Filter: A special case of penalized spline smoothing
Robert L. Paige, A. Alexandre Trindade
Electron. J. Statist. 4: 856-874 (2010). DOI: 10.1214/10-EJS570


We prove that the Hodrick-Prescott Filter (HPF), a commonly used method for smoothing econometric time series, is a special case of a linear penalized spline model with knots placed at all observed time points (except the first and last) and uncorrelated residuals. This equivalence then furnishes a rich variety of existing data-driven parameter estimation methods, particularly restricted maximum likelihood (REML) and generalized cross-validation (GCV). This has profound implications for users of HPF who have hitherto typically relied on subjective choice, rather than estimation, for the smoothing parameter. By viewing estimates as roots of an appropriate quadratic estimating equation, we also present a new approach for constructing confidence intervals for the smoothing parameter. The method is akin to a parametric bootstrap where Monte Carlo simulation is replaced by saddlepoint approximation, and provides a fast and accurate alternative to exact methods, when they exist, e.g. REML. More importantly, it is also the only computationally feasible method when no other methods, exact or otherwise, exist, e.g. GCV. The methodology is demonstrated on the Gross National Product (GNP) series originally analyzed by Hodrick and Prescott (1997). With proper attention paid to residual correlation structure, we show that REML-based estimation delivers an appropriate smooth for both the GNP series and its returns.


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Robert L. Paige. A. Alexandre Trindade. "The Hodrick-Prescott Filter: A special case of penalized spline smoothing." Electron. J. Statist. 4 856 - 874, 2010.


Published: 2010
First available in Project Euclid: 15 September 2010

zbMATH: 1329.62200
MathSciNet: MR2721036
Digital Object Identifier: 10.1214/10-EJS570

Primary: 62F25
Secondary: 62G09

Keywords: econometric smoothing , gross national product , parametric bootstrap confidence interval , saddlepoint approximation , Semiparametric model

Rights: Copyright © 2010 The Institute of Mathematical Statistics and the Bernoulli Society

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