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The sampling properties of conditional independence graphs for structural vector autoregressions

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  • Marco Reale
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    Abstract

    Structural vector autoregressions allow contemporaneous series dependence and assume errors with no contemporaneous correlation. Models of this form, that also have a recursive structure, can be described by a directed acyclic graph. An important tool for identification of these models is the conditional independence graph constructed from the contemporaneous and lagged values of the process. We determine the large-sample properties of statistics used to test for the presence of links in this graph. A simple example illustrates how these results may be applied. Copyright Biometrika Trust 2002, Oxford University Press.

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    Bibliographic Info

    Article provided by Biometrika Trust in its journal Biometrika.

    Volume (Year): 89 (2002)
    Issue (Month): 2 (June)
    Pages: 457-461

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    Handle: RePEc:oup:biomet:v:89:y:2002:i:2:p:457-461

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    Cited by:
    1. Carretta, Alessandro & Farina, Vincenzo & Graziano, Elvira Anna & Reale, Marco, 2011. "Does investor attention influence stock market activity? The case of spin-off deals," MPRA Paper 33545, University Library of Munich, Germany.
    2. Alethea Rea & William Rea & Marco Reale & Carl Scarrott, 2012. "A comparison of Spillover Effects before, during and after the 2008 Financial Crisis," Working Papers in Economics 12/03, University of Canterbury, Department of Economics and Finance.
    3. Selva Demiralp & Kevin Hoover & Stephen Perez, 2014. "Still puzzling: evaluating the price puzzle in an empirically identified structural vector autoregression," Empirical Economics, Springer, vol. 46(2), pages 701-731, March.

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