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A Bootstrap Method for Identifying and Evaluating a Structural Vector Autoregression


  • Selva Demiralp
  • Kevin D. Hoover
  • Stephen J. Perez


Graph-theoretic methods of causal search based on the ideas of Pearl (2000), Spirtes "et al". (2000), and others have been applied by a number of researchers to economic data, particularly by Swanson and Granger (1997) to the problem of finding a data-based contemporaneous causal order for the structural vector autoregression, rather than, as is typically done, assuming a weakly justified Choleski order. Demiralp and Hoover (2003) provided Monte Carlo evidence that such methods were effective, provided that signal strengths were sufficiently high. Unfortunately, in applications to actual data, such Monte Carlo simulations are of limited value, as the causal structure of the true data-generating process is necessarily unknown. In this paper, we present a bootstrap procedure that can be applied to actual data (i.e. without knowledge of the true causal structure). We show with an applied example and a simulation study that the procedure is an effective tool for assessing our confidence in causal orders identified by graph-theoretic search algorithms. Copyright (c) Blackwell Publishing Ltd and the Department of Economics, University of Oxford, 2008.

Suggested Citation

  • Selva Demiralp & Kevin D. Hoover & Stephen J. Perez, 2008. "A Bootstrap Method for Identifying and Evaluating a Structural Vector Autoregression," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 70(4), pages 509-533, August.
  • Handle: RePEc:bla:obuest:v:70:y:2008:i:4:p:509-533

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    References listed on IDEAS

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    5. Kevin D. Hoover & Stephen J. Perez, 1999. "Data mining reconsidered: encompassing and the general-to-specific approach to specification search," Econometrics Journal, Royal Economic Society, vol. 2(2), pages 167-191.
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    7. Bessler, David A & Loper, Nathan, 2001. "Economic Development: Evidence from Directed Graphs," Manchester School, University of Manchester, vol. 69(4), pages 457-476, September.
    8. Fair Ray C, 2003. "Bootstrapping Macroeconometric Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 7(4), pages 1-26, December.
    9. David A. Bessler & Seongpyo Lee, 2002. "Money and prices: U.S. Data 1869-1914 (A study with directed graphs)," Empirical Economics, Springer, vol. 27(3), pages 427-446.
    10. Michael S. Haigh & Nikos K. Nomikos & David A. Bessler, 2004. "Integration and Causality in International Freight Markets: Modeling with Error Correction and Directed Acyclic Graphs," Southern Economic Journal, Southern Economic Association, vol. 71(1), pages 145-162, July.
    11. Titus Awokuse, 2006. "Export-led growth and the Japanese economy: evidence from VAR and directed acyclic graphs," Applied Economics, Taylor & Francis Journals, vol. 38(5), pages 593-602.
    12. Hoover, Kevin D., 2003. "Some causal lessons from macroeconomics," Journal of Econometrics, Elsevier, vol. 112(1), pages 121-125, January.
    13. Jian Yang & David A. Bessler, 2004. "The International Price Transmission in Stock Index Futures Markets," Economic Inquiry, Western Economic Association International, vol. 42(3), pages 370-386, July.
    14. Bessler, David A. & Yang, Jian, 2003. "The structure of interdependence in international stock markets," Journal of International Money and Finance, Elsevier, vol. 22(2), pages 261-287, April.
    15. Hoover, Kevin D., 2005. "Automatic Inference Of The Contemporaneous Causal Order Of A System Of Equations," Econometric Theory, Cambridge University Press, vol. 21(01), pages 69-77, February.
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    Cited by:

    1. Hogun Chong & Mary Zey & David A. Bessler, 2010. "On corporate structure, strategy, and performance: a study with directed acyclic graphs and PC algorithm," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 31(1), pages 47-62.
    2. Chauvet, Marcelle & Tierney, Heather L. R., 2007. "Real Time Changes in Monetary Policy," MPRA Paper 16199, University Library of Munich, Germany, revised Apr 2009.
    3. Pu Chen & Chih-Ying Hsiao, 2010. "Causal Inference for Structural Equations: With an Application to Wage-Price Spiral," Computational Economics, Springer;Society for Computational Economics, vol. 36(1), pages 17-36, June.
    4. Henry L. Bryant & David A. Bessler & Michael S. Haigh, 2009. "Disproving Causal Relationships Using Observational Data," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(3), pages 357-374, June.
    5. Phiromswad, Piyachart, 2015. "Measuring monetary policy with empirically grounded restrictions: An application to Thailand," Journal of Asian Economics, Elsevier, vol. 38(C), pages 104-113.
    6. Andrew Rettenmaier & Zijun Wang, 2013. "What determines health: a causal analysis using county level data," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 14(5), pages 821-834, October.
    7. Alessio Moneta & Doris Entner & Patrik O. Hoyer & Alex Coad, 2013. "Causal Inference by Independent Component Analysis: Theory and Applications," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 75(5), pages 705-730, October.
    8. Wongboonsin, Kua & Phiromswad, Piyachart, 2017. "Searching for empirical linkages between demographic structure and economic growth," Economic Modelling, Elsevier, vol. 60(C), pages 364-379.
    9. Kevin Hoover, 2005. "Economic Theory and Causal Inference," Working Papers 64, University of California, Davis, Department of Economics.
    10. Piyachart Phiromswad, 2014. "Measuring monetary policy with empirically grounded identifying restrictions," Empirical Economics, Springer, vol. 46(2), pages 681-699, March.
    11. Wang, Zijun, 2012. "The causal structure of bond yields," The Quarterly Review of Economics and Finance, Elsevier, vol. 52(1), pages 93-102.
    12. 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.
    13. Piyachart Phiromswad & Takeshi Yagihashi, 2016. "Empirical identification of factor models," Empirical Economics, Springer, vol. 51(2), pages 621-658, September.
    14. Jinjarak, Yothin, 2013. "Supply Chains and Credit-Market Shocks: Some Implications for Emerging Markets," ADBI Working Papers 443, Asian Development Bank Institute.

    More about this item

    JEL classification:

    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation


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