A Bootstrap Method for Identifying and Evaluating a Structural Vector Autoregression
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.Download Info
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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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Related research
Keywords:Other versions of this item:
- Hoover, Kevin & Demiralp, Selva & Perez, Stephen J., 2006. "A Bootstrap Method for Identifying and Evaluating a Structural Vector Autoregression," Working Papers 06-14, University of California at Davis, Department of Economics.
- Kevin Hoover & Selva Demiralp & Stephen J. Perez, 2006. "A Bootstrap Method for Identifying and Evaluating a Structural Vector Autoregression," Working Papers 614, University of California, Davis, Department of Economics.
- 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
- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
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Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.Cited by:
- Kevin Hoover, 2005.
"Economic Theory and Causal Inference,"
Working Papers
64, University of California, Davis, Department of Economics.
- Hoover, Kevin D., 2005. "Economic Theory and Causal Inference," Working Papers 06-4, University of California at Davis, Department of Economics.
- 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.
- Chauvet, Marcelle & Tierney, Heather L. R., 2007. "Real Time Changes in Monetary Policy," MPRA Paper 16199, University Library of Munich, Germany, revised Apr 2009.
- 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.
- 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).
- 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, 06.
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