The Extended Hodrick-Prescott (HP) Filter for Spatial Regression Smoothing
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.
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- Richard Sellner & Wolfgang Polasek, 2011.
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- Polasek, Wolfgang & Sellner, Richard, 2011. "Does Globalization Affect Regional Growth? Evidence for NUTS-2 Regions in EU-27," Economics Series 266, Institute for Advanced Studies.
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- Wolfgang Polasek, 2012.
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DANUBE: Law and Economics Review,
European Association Comenius - EACO, issue 1, pages 25-52, March.
- Wolfgang Polasek, 2011. "MCMC Estimation of Extended Hodrick-Prescott (HP) Filtering Models," Working Paper Series 25_11, The Rimini Centre for Economic Analysis.
- King, Robert G. & Rebelo, Sergio T., 1993. "Low frequency filtering and real business cycles," Journal of Economic Dynamics and Control, Elsevier, vol. 17(1-2), pages 207-231.
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