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

Polasek, Wolfgang (November 2011) The Extended Hodrick-Prescott (HP) Filter for Spatial Regression Smoothing. IHS Economics Series 275

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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 eachobservation 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.;

Item Type: IHS Series
Keywords: 'Hodrick-Prescott (HP) smoothers' 'Smoothed square loss function' 'Spatial smoothing' 'Smoothness prior' '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 18:23
URI: http://irihs.ihs.ac.at/id/eprint/2096

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