The Hodrick-Prescott (HP) Filter as a Bayesian Regression Model

Polasek, Wolfgang (November 2011) The Hodrick-Prescott (HP) Filter as a Bayesian Regression Model. IHS Economics Series 277


Download (475kB) | Preview

Abstract or Table of Contents

Abstract: The Hodrick-Prescott (HP) method is a popular smoothing method for economic time series to get a smooth or long-term component of stationary series like growth rates. We show that the HP smoother can be viewed as a Bayesian linear model with a strong prior using differencing matrices for the smoothness component. The HP smoothing approach requires a linear regression model with a Bayesian conjugate multi-normal-gamma distribution. The Bayesian approach also allows to make predictions of the HP smoother on both ends of the time series. Furthermore, we show how Bayes tests can determine the order of smoothness in the HP smoothing model. The extended HP smoothing approach is demonstrated for the non-stationary (textbook) airline passenger time series. Thus, the Bayesian extension of the HP model defines a new class of model-based smoothers for (non-stationary) time series and spatial models.;

Item Type: IHS Series
Keywords: 'Hodrick-Prescott (HP) smoothers' 'Model selection by marginal likelihoods' 'Multi-normal-gamma distribution' 'Spatial sales growth data' 'Bayesian econometrics'
Classification Codes (e.g. JEL): C11, C15, C52, E17, R12
Status: Published
Date Deposited: 26 Sep 2014 10:39
Last Modified: 22 Jul 2017 00:29

Actions (login required)

View Item View Item