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The sampling properties of conditional independence graphs for I(1) structural VAR models

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  • Granville Tunnicliffe Wilson
  • Marco Reale

Abstract

. Structural vector autoregressions allow dependence among contemporaneous variables. If such models have a recursive structure, the relationships among the variables can be represented by directed acyclic graphs. The identification of these relationships for stationary series may be enabled by the examination of the conditional independence graph constructed from sample partial autocorrelations of the observed series. In this article, we extend this approach to the case when the series follows an I(1) vector autoregression. For such a model, estimated regression coefficients may have non‐standard asymptotic distributions and in small samples this affects the distribution of sample partial autocorrelations. We show that, nevertheless, in large samples, exactly the same inference procedures may be applied as in the stationary case.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:jtsera:v:29:y:2008:i:5:p:802-810
    DOI: 10.1111/j.1467-9892.2008.00583.x
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    References listed on IDEAS

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    1. Titus O. Awokuse & David A. Bessler, 2003. "Vector Autoregressions, Policy Analysis, and Directed Acyclic Graphs: An Application to the U.S. Economy," Journal of Applied Economics, Universidad del CEMA, vol. 6, pages 1-24, May.
    2. Marco Reale, 2002. "The sampling properties of conditional independence graphs for structural vector autoregressions," Biometrika, Biometrika Trust, vol. 89(2), pages 457-461, June.
    3. 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.
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    Cited by:

    1. Nikolay Arefiev, 2014. "A Theory Of Data-Oriented Identification With A Svar Application," HSE Working papers WP BRP 79/EC/2014, National Research University Higher School of Economics.
    2. Matteo Fragetta & Giovanni Melina, 2013. "Identification of monetary policy in SVAR models: a data-oriented perspective," Empirical Economics, Springer, vol. 45(2), pages 831-844, October.
    3. 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.
    4. Benjamin Poignard & Manabu Asai, 2023. "Estimation of high-dimensional vector autoregression via sparse precision matrix," The Econometrics Journal, Royal Economic Society, vol. 26(2), pages 307-326.
    5. Matteo Fragetta & Giovanni Melina, 2010. "The Effects of Fiscal Shocks in SVAR Models: A Graphical Modelling Approach," Birkbeck Working Papers in Economics and Finance 1006, Birkbeck, Department of Economics, Mathematics & Statistics.
    6. Nikolay Arefiev, 2016. "Identification of Monetary Policy Shocks within a Svar Using Restrictions Consistent with a DSGE Model," HSE Working papers WP BRP 125/EC/2016, National Research University Higher School of Economics.
    7. 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.

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