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

Author

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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.

Suggested Citation

  • Polasek, Wolfgang, 2011. "The Extended Hodrick-Prescott (HP) Filter for Spatial Regression Smoothing," Economics Series 275, Institute for Advanced Studies.
  • Handle: RePEc:ihs:ihsesp:275
    as

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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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    References listed on IDEAS

    as
    1. Richard Sellner & Wolfgang Polasek, 2011. "Does Globalization affect Regional Growth? Evidence for NUTS-2 Regions in EU-27," ERSA conference papers ersa11p819, European Regional Science Association.
    2. Uhlig, H.F.H.V.S. & Ravn, M., 1997. "On Adjusting the H-P Filter for the Frequency of Observations," Discussion Paper 1997-50, Tilburg University, Center for Economic Research.
    3. Hodrick, Robert J & Prescott, Edward C, 1997. "Postwar U.S. Business Cycles: An Empirical Investigation," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 29(1), pages 1-16, February.
    4. Wolfgang Polasek, 2012. "MCMC Estimation of Extended Hodrick-Prescott (HP) Filtering Models," DANUBE: Law and Economics Review, European Association Comenius - EACO, issue 1, pages 25-52, March.
    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.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

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

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)

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