A Bootstrap Method for Identifying and Evaluating a Structural Vector Autoregression
AbstractGraph-theoretic methods of causal search based in the ideas of Pearl (2000), Spirtes, Glymour, and Scheines (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 autoregression (SVAR), 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, since 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 procedures.
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Bibliographic InfoPaper provided by University of California at Davis, Department of Economics in its series Working Papers with number 06-14.
Date of creation: Mar 2006
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- 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, 08.
- 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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- Kevin Hoover, 2005.
"Economic Theory and Causal Inference,"
64, University of California, 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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