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Prognose uni- und multivariater Zeitreihen

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  • Manfred Deistler
  • Klaus Neusser

Abstract

Der Aufsatz bietet eine Zusammenfassung der theoretischen Grundlagen der linearen Kleinst-Quadrate-Prognose im Kontext von stationären Prozessen, insbesondere im Zusammenhang von ARMA bzw. ARMAX Systemen. In einem ersten Schritt wird das Prognoseproblem unter der Voraussetzung, dass die zweiten Momente bekannt sind, behandelt. Da diese jedoch meist nicht bekannt sind, geht das Prognoseproblem mit einem Identifikationsproblem einher. Dieses Problem wird eingehend anhand von multivariaten AR-, ARMA- und ARMAX-System erläutert. Da bei der praktischen Anwendung noch andere Gesichtspunkte (a priori Information, Fristigkeit, Aufwand, Geschwindigkeit, etc.) eine Rolle spielen und die Methoden daher eventuell adaptiert werden müssen, werden einige bei der praktischen Anwendung auftretende Probleme anhand der Prognose makroökonomischer und betriebswirtschaftlicher Zeitreihen (Absatzprognose) kurz illustriert.

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Bibliographic Info

Paper provided by Universitaet Bern, Departement Volkswirtschaft in its series Diskussionsschriften with number dp0401.

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Date of creation: Jan 2004
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Handle: RePEc:ube:dpvwib:dp0401

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Keywords: Prognose; Identifikation; ARMAX-Systeme;

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  1. Hendry, David F & Michael P. Clements, 2002. "Economic Forecasting: Some Lessons from Recent Research," Royal Economic Society Annual Conference 2002 99, Royal Economic Society.
  2. Klaus NEUSSER, 1990. "Testing the Long-Run Implications of the Neoclassical Growth Model," Vienna Economics Papers vie9002, University of Vienna, Department of Economics.
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