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Informationskriterien und Volatility Clustering

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  • Jacobi, Frank

Abstract

Ein wichtiges Problem in der statistischen Analyse ist die Auswahl eines passenden Mo-dells. Im Kontext linearer ARIMA-Modelle kann gezeigt werden, dass - die Gültigkeit bestimmter Regularitätsbedingungen vorausgesetzt - die Minimierung des Schwarz-Kriteriums zu einer konsistenten Wahl der Anzahl der Parameter in einem Modell führt, wohingegen die Schätzung der Parameterzahl mit Hilfe des Akaike-Kriteriums tendenziell zu große Modelle liefert. Ziel dieser Analyse ist es, mit Hilfe von Monte-Carlo-Experimenten die Eigenschaften des Akaike- und des Schwarz-Informationskriteriums zu untersuchen, wenn der datengenerierende Prozess GARCH-Störungen aufweist.

Suggested Citation

  • Jacobi, Frank, 2005. "Informationskriterien und Volatility Clustering," Arbeitspapiere des Instituts für Statistik und Ökonometrie 32, Johannes Gutenberg-Universität Mainz, Institut für Statistik und Ökonometrie.
    • Handle: RePEc:zbw:maista:32
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    File URL: https://www.econstor.eu/bitstream/10419/32036/1/504233637.pdf
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    References listed on IDEAS

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    1. Lange, Yvonne, 2000. "Ein logistisches Regressionsmodell zur Analyse der Verkehrmittelwahl im Raum Mainz," Arbeitspapiere des Instituts für Statistik und Ökonometrie 22, Johannes Gutenberg-Universität Mainz, Institut für Statistik und Ökonometrie.
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    Cited by:

    1. Schulze, Peter M., 2009. "Seasonal unit root tests for the monthly container transshipment of the port of Hamburg," Arbeitspapiere des Instituts für Statistik und Ökonometrie 45, Johannes Gutenberg-Universität Mainz, Institut für Statistik und Ökonometrie.

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    1. Schulze, Peter M., 2009. "Seasonal unit root tests for the monthly container transshipment of the port of Hamburg," Arbeitspapiere des Instituts für Statistik und Ökonometrie 45, Johannes Gutenberg-Universität Mainz, Institut für Statistik und Ökonometrie.

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