The extended Hodrick-Prescott (HP) filter for spatial regression smoothing

Arbeitspapier

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 model-based 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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Reihe Ökonomie / Economics Series ; No. 275

Classification and Topics

• Classification: Wirtschaft ; Bayesian Analysis: General ; Statistical Simulation Methods: General ; Model Evaluation, Validation, and Selection ; General Aggregative Models: Forecasting and Simulation: Models and Applications ; Size and Spatial Distributions of Regional Economic Activity
• Subject: Hodrick-Prescott (HP) smoothers ; smoothed square loss function ; spatial smoothing ; smoothness prior ; Bayesian econometrics

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Polasek, Wolfgang
• Event: Veröffentlichung
• (who): Institute for Advanced Studies (IHS)
• (where): Vienna
• (when): 2011

Further information

• Handle: http://hdl.handle.net/10419/68527
• Last update: 10.03.2025, 11:42 AM CET