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Searching for the Causal Structure of a Vector Autoregression

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  • Selva Demiralp
  • Kevin D. Hoover

Abstract

We provide an accessible introduction to graph-theoretic methods for causal analysis. Building on the work of Swanson and Granger ("Journal of the American Statistical Association", Vol. 92, pp. 357-367, 1997), and generalizing to a larger class of models, we show how to apply graph-theoretic methods to selecting the causal order for a structural vector autoregression (SVAR). We evaluate the PC (causal search) algorithm in a Monte Carlo study. The PC algorithm uses tests of conditional independence to select among the possible causal orders - or at least to reduce the admissible causal orders to a narrow equivalence class. Our findings suggest that graph-theoretic methods may prove to be a useful tool in the analysis of SVARs. Copyright 2003 Blackwell Publishing Ltd.

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Bibliographic Info

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

Volume (Year): 65 (2003)
Issue (Month): s1 (December)
Pages: 745-767

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Handle: RePEc:bla:obuest:v:65:y:2003:i:s1:p:745-767

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References

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  1. Kevin D. Hoover & Stephen J. Perez, . "Data Mining Reconsidered: Encompassing And The General-To-Specific Approach To Specification Search," Department of Economics 97-27, California Davis - Department of Economics.
  2. Cooley, Thomas F. & Leroy, Stephen F., 1985. "Atheoretical macroeconometrics: A critique," Journal of Monetary Economics, Elsevier, vol. 16(3), pages 283-308, November.
  3. 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.
  4. David Hendry & Hans-Martin Krolzig, 2000. "Computer Automation of General-to-Specific Model Selection Procedures," Economics Series Working Papers 3, University of Oxford, Department of Economics.
  5. Kevin D. Hoover & Stephen J. Perez, 2004. "Truth and Robustness in Cross-country Growth Regressions," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 66(5), pages 765-798, December.
  6. David F. Hendry & Hans-Martin Krolzig, 1999. "Improving on 'Data mining reconsidered' by K.D. Hoover and S.J. Perez," Econometrics Journal, Royal Economic Society, vol. 2(2), pages 202-219.
  7. Steven M. Sheffrin & Robert K. Triest, 1995. "A new approach to causality and economic growth," Working Papers 95-12, Federal Reserve Bank of Boston.
  8. Kevin Hoover & Stephen Perez, 2001. "Three attitudes towards data mining," Journal of Economic Methodology, Taylor & Francis Journals, vol. 7(2), pages 195-210.
  9. Hans-Martin Krolzig, 2000. "Computer Automation of General-to-Specific Model Selection Procedures," Econometric Society World Congress 2000 Contributed Papers 0411, Econometric Society.
  10. Hoover,Kevin D., 2001. "Causality in Macroeconomics," Cambridge Books, Cambridge University Press, number 9780521002882, October.
  11. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
  12. Leamer, Edward E., 1985. "Vector autoregressions for causal inference?," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 22(1), pages 255-304, January.
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