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The sampling properties of conditional independence graphs for structural vector autoregressions

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  • Marco Reale

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.

Suggested Citation

  • Marco Reale, 2002. "The sampling properties of conditional independence graphs for structural vector autoregressions," Biometrika, Biometrika Trust, vol. 89(2), pages 457-461, June.
    • Handle: RePEc:oup:biomet:v:89:y:2002:i:2:p:457-461
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    Citations

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    Cited by:

    1. 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.
    2. Oxley, Les & Reale, Marco & Wilson, Granville Tunnicliffe, 2009. "Constructing structural VAR models with conditional independence graphs," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(9), pages 2910-2916.
    3. Alessandro Carretta & Vincenzo Farina & Elvira Anna Graziano & Marco Reale, 2013. "Does Investor Attention Influence Stock Market Activity? The Case of Spin-Off Deals," Palgrave Macmillan Studies in Banking and Financial Institutions, in: Alessandro Carretta & Gianluca Mattarocci (ed.), Asset Pricing, Real Estate and Public Finance over the Crisis, chapter 1, pages 7-24, Palgrave Macmillan.
    4. Marco Reale & Granville Tunnicliffe Wilson, 2001. "Identification of vector AR models with recursive structural errors using conditional independence graphs," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 10(1), pages 49-65, January.
    5. Granville Tunnicliffe Wilson & Marco Reale, 2008. "The sampling properties of conditional independence graphs for I(1) structural VAR models," Journal of Time Series Analysis, Wiley Blackwell, vol. 29(5), pages 802-810, September.
    6. 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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