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

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  • Polasek, Wolfgang

    (Department of Economics and Finance, Institute for Advanced Studies, Vienna, Austria and University of Porto, Portugal)

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.

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File URL: http://www.ihs.ac.at/publications/eco/es-275.pdf
File Function: First version, 2011
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Bibliographic Info

Paper provided by Institute for Advanced Studies in its series Economics Series with number 275.

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Length: 19 pages
Date of creation: Nov 2011
Date of revision:
Handle: RePEc:ihs:ihsesp:275

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Related research

Keywords: Hodrick-Prescott (HP) smoothers; smoothed square loss function; spatial smoothing; smoothness prior; bayesian econometrics;

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  1. Wolfgang Polasek, 2011. "MCMC Estimation of Extended Hodrick-Prescott (HP) Filtering Models," Working Paper Series 25_11, The Rimini Centre for Economic Analysis.
  2. Richard Sellner & Wolfgang Polasek, 2011. "Does Globalization a ffect Regional Growth? Evidence for NUTS-2 Regions in EU-27," ERSA conference papers ersa11p819, European Regional Science Association.
  3. Robert J. Hodrick & Edward Prescott, 1981. "Post-War U.S. Business Cycles: An Empirical Investigation," Discussion Papers 451, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
  4. Morten O. Ravn & Harald Uhlig, 2001. "On Adjusting the HP-Filter for the Frequency of Observations," CESifo Working Paper Series 479, CESifo Group Munich.
  5. 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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