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

Wolfgang Polasek

No 275, Economics Series from Institute for Advanced Studies

Abstract: The extended Hodrick-Prescott (HP) method was developed by Polasek (2011) for a class of data smoother based on second order smoothness. This paper develops a new extended HP smoothing model that can be applied for spatial smoothing problems. In Bayesian smoothing we need a 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 modelbased 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)
Pages: 19 pages
Date: 2011-11
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https://irihs.ihs.ac.at/id/eprint/2096 First version, 2011 (application/pdf)

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Working Paper: The Extended Hodrick-Prescott (HP) Filter for Spatial Regression Smoothing (2011)
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