Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/68522
Authors: 
Polasek, Wolfgang
Year of Publication: 
2011
Series/Report no.: 
Economics Series, Institute for Advanced Studies 277
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.
Subjects: 
Hodrick-Prescott (HP) smoothers
model selection by marginal likelihoods
multi-normalgamma distribution
Spatial sales growth data
Bayesian econometrics
JEL: 
C11
C15
C52
E17
R12
Document Type: 
Working Paper

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