The Extended Hodrick-Prescott (HP) Filter for Spatial Regression Smoothing

Wolfgang Polasek

Working Paper series from Rimini Centre for Economic Analysis

Abstract: The Hodrick-Prescott (HP) method is a popular smoothing method for economic time series to get a long-term component of stationary series like growth rates. The new extended HP smoothing model is applied to data-sets with an underlying metric and requires a Bayesian linear regression model with a strong prior based on differencing matrices for the smoothness parameter and a weak prior for the regression part. We define a Bayesian spatial smoothing model with neighbors for each observation and we define a smoothness prior similar to the HP filter in time series. This opens a new approach to model-based smoothers for time series and spatial models based on MCMC. We apply it to the NUTS-2 regions of the European Union for regional GDP and GDP per capita, where the fixed effects are removed by an extended HP smoothing model.

Keywords: Hodrick-Prescott (HP) smoothers; smoothed square loss function; spatial smoothing; smoothness prior; Bayesian econometrics (search for similar items in EconPapers)
JEL-codes: C11 C15 C52 E17 R12 (search for similar items in EconPapers)
Date: 2011-11
New Economics Papers: this item is included in nep-ecm, nep-geo and nep-ure
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Working Paper: The Extended Hodrick-Prescott (HP) Filter for Spatial Regression Smoothing (2011)
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Persistent link: https://EconPapers.repec.org/RePEc:rim:rimwps:45_11

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