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A Bootstrap Method for Identifying and Evaluating a Structural Vector Autoregression

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

Abstract

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.

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Bibliographic Info

Article provided by Department of Economics, University of Oxford in its journal Oxford Bulletin of Economics and Statistics.

Volume (Year): 70 (2008)
Issue (Month): 4 (08)
Pages: 509-533

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Handle: RePEc:bla:obuest:v:70:y:2008:i:4:p:509-533

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Cited by:
  1. Kevin Hoover, 2005. "Economic Theory and Causal Inference," Working Papers 64, University of California, Davis, Department of Economics.
  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, Society for Computational Economics, vol. 36(1), pages 17-36, June.
  4. Bryant, Henry L. & Bessler, David A. & Haigh, Michael S., 2006. "Disproving Causal Relationships Using Observational Data," 2006 Annual meeting, July 23-26, Long Beach, CA 21166, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
  5. Wang, Zijun, 2012. "The causal structure of bond yields," The Quarterly Review of Economics and Finance, Elsevier, vol. 52(1), pages 93-102.
  6. Andrew Rettenmaier & Zijun Wang, 2013. "What determines health: a causal analysis using county level data," The European Journal of Health Economics, Springer, vol. 14(5), pages 821-834, October.
  7. 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.
  8. 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.
  9. Jinjarak, Yothin, 2013. "Supply Chains and Credit-Market Shocks: Some Implications for Emerging Markets," ADBI Working Papers 443, Asian Development Bank Institute.
  10. Piyachart Phiromswad, 2014. "Measuring monetary policy with empirically grounded identifying restrictions," Empirical Economics, Springer, vol. 46(2), pages 681-699, March.

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