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      <record key="001" att1="001" value="184577" att2="184577">001   184577</record>
      <field key="037" subkey="x">englisch</field>
      <field key="050" subkey="x">Forschungsbericht</field>
      <field key="076" subkey="">Ökonomie</field>
      <field key="079" subkey="y">http://www.ihs.ac.at/publications/eco/es-277.pdf</field>
      <field key="079" subkey="z">Polasek, Wolfgang, The Hodrick-Prescott (HP) Filter as a Bayesian Regression Model (pdf)</field>
      <field key="079" subkey="y">http://ideas.repec.org/p/ihs/ihsesp/277.html</field>
      <field key="079" subkey="z">Institute for Advanced Studies. Economics Series; 277 (RePEc)</field>
      <field key="100" subkey="">Polasek, Wolfgang</field>
      <field key="103" subkey="">Department of Economics and Finance, Institute for Advanced Studies, Vienna, Austria and University of Porto, Portugal</field>
      <field key="331" subkey="">The Hodrick-Prescott (HP) Filter as a Bayesian Regression Model</field>
      <field key="403" subkey="">1. Ed.</field>
      <field key="410" subkey="">Wien</field>
      <field key="412" subkey="">Institut für Höhere Studien</field>
      <field key="425" subkey="">2011, November</field>
      <field key="433" subkey="">19 pp.</field>
      <field key="451" subkey="">Institut für Höhere Studien; Reihe Ökonomie; 277</field>
      <field key="451" subkey="h">Kunst, Robert M. (Ed.) ; Fisher, Walter (Assoc. Ed.) ; Ritzberger, Klaus (Assoc. Ed.)</field>
      <field key="461" subkey="">Economics Series</field>
      <field key="517" subkey="c">from the Table of Contents: Introduction; The HP filter as minimizer of a loss function; The HP filter as a Bayesian smoothness</field>
      <field key="reg" subkey="r">ession model; Model selection and Bayes testing; Summary; Appendix: Results on Combination of Quadratic Forms; R program for</field>
      <field key="log" subkey="">marginal likelihoods; References;</field>
      <field key="542" subkey="">1605-7996</field>
      <field key="544" subkey="">IHSES 277</field>
      <field key="700" subkey="">C11</field>
      <field key="700" subkey="">C15</field>
      <field key="700" subkey="">C52</field>
      <field key="700" subkey="">E17</field>
      <field key="700" subkey="">R12</field>
      <field key="720" subkey="">Hodrick-Prescott (HP) smoothers</field>
      <field key="720" subkey="">Model selection by marginal likelihoods</field>
      <field key="720" subkey="">Multi-normal-gamma distribution</field>
      <field key="720" subkey="">Spatial sales growth data</field>
      <field key="720" subkey="">Bayesian econometrics</field>
      <field key="753" subkey="">Abstract: The Hodrick-Prescott (HP) method is a popular smoothing method for economic time series to get a smooth or long-term</field>
      <field key="com" subkey="p">onent of stationary series like growth rates. We show that the HP smoother can be viewed as a Bayesian linear model with a</field>
      <field key="str" subkey="o">ng prior using differencing matrices for the smoothness component. The HP smoothing approach requires a linear regression</field>
      <field key="mod" subkey="e">l with a Bayesian conjugate multi-normal-gamma distribution. The Bayesian approach also allows to make predictions of the HP</field>
      <field key="smo" subkey="o">ther on both ends of the time series. Furthermore, we show how Bayes tests can determine the order of smoothness in the HP</field>
      <field key="smo" subkey="o">thing model. The extended HP smoothing approach is demonstrated for the non-stationary (textbook) airline passenger time</field>
      <field key="ser" subkey="i">es. Thus, the Bayesian extension of the HP model defines a new class of model-based smoothers for (non-stationary) time</field>
      <field key="ser" subkey="i">es and spatial models.;</field>
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