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      <record key="001" att1="001" value="192521" att2="192521">001   192521</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-309.pdf</field>
      <field key="079" subkey="z">Costantini, Mauro - et al., Forecast combinations in a DSGE-VAR lab (pdf)</field>
      <field key="079" subkey="y">http://ideas.repec.org/p/ihs/ihsesp/309.html</field>
      <field key="079" subkey="z">Institute for Advanced Studies. Economics Series; 309 (RePEc)</field>
      <field key="100" subkey="">Costantini, Mauro</field>
      <field key="103" subkey="">Department of Economics and Finance, Brunel University</field>
      <field key="104" subkey="a">Gunter, Ulrich</field>
      <field key="107" subkey="">Department of Tourism and Service Management, MODUL University Vienna</field>
      <field key="108" subkey="a">Kunst, Robert M.</field>
      <field key="111" subkey="">Department of Economics and Finance, Institute for Advanced Studies, Vienna and Department of Economics, University of Vienna</field>
      <field key="331" subkey="">Forecast combinations in a DSGE-VAR lab</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="">2014, December</field>
      <field key="433" subkey="">57 pp.</field>
      <field key="451" subkey="">Institut für Höhere Studien; Reihe Ökonomie; 309</field>
      <field key="451" subkey="h">Kunst, Robert M. (Ed.) ; Reiter, Michael (Assoc. Ed.) ; Uysal, Selver Derya (Assoc. Ed.)</field>
      <field key="461" subkey="">Economics Series</field>
      <field key="517" subkey="c">from the Table of Contents: Introduction; Methodology; The data-generating process; Results; Application to empirical data;</field>
      <field key="Con" subkey="c">lusion; A medium-scale DSGE model;</field>
      <field key="542" subkey="">1605-7996</field>
      <field key="544" subkey="">IHSES 309</field>
      <field key="720" subkey="">Combining forecasts</field>
      <field key="720" subkey="">Encompassing tests</field>
      <field key="720" subkey="">Model selection</field>
      <field key="720" subkey="">Time series</field>
      <field key="720" subkey="">DSGE-VAR model</field>
      <field key="753" subkey="">Abstract: We explore the benefits of forecast combinations based on forecast-encompassing tests compared to simple averages and</field>
      <field key="to" subkey="B">ates-Granger combinations. We also consider a new combination method that fuses test-based and Bates-Granger weighting. For a</field>
      <field key="rea" subkey="l">istic simulation design, we generate multivariate time-series samples from a macroeconomic DSGE-VAR model. Results generally</field>
      <field key="sup" subkey="p">ort Bates-Granger over uniform weighting, whereas benefits of test-based weights depend on the sample size and on the</field>
      <field key="pre" subkey="d">iction horizon. In a corresponding application to real-world data, simple averaging performs best. Uniform averages may be</field>
      <field key="the" subkey="">weighting scheme that is most robust to empirically observed irregularities.;</field>
    </SEQUENTIAL>
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