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Searching for the Causal Structure of a Vector Autoregression

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  • Kevin Hoover
  • Selva Demiralp

    (Department of Economics, University of California Davis)

Abstract

Vector autoregressions (VARs) are economically interpretable only when identified by being transformed into a structural form (the SVAR) in which the contemporaneous variables stand in a well-defined causal order. These identifying transformations are not unique. It is widely believed that practitioners must choose among them using a priori theory or other criteria not rooted in the data under analysis. We show how to apply graph-theoretic methods of searching for causal structure based on relations of conditional independence to select among the possible causal orders – or at least to reduce the admissible causal orders to a narrow equivalence class. The graph-theoretic approaches were developed by computer scientists and philosophers (Pearl, Glymour, Spirtes among others) and applied to cross-sectional data. We provide an accessible introduction to this work. Then building on the work of Swanson and Granger (1997), we show how to apply it to searching for the causal order of an SVAR. We present simulation results to show how the efficacy of the search method algorithm varies with signal strength for realistic sample lengths. Our findings suggest that graph-theoretic methods may prove to be a useful tool in the analysis of SVARs.

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Bibliographic Info

Paper provided by University of California, Davis, Department of Economics in its series Working Papers with number 33.

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Length: 41
Date of creation: 18 Mar 2003
Date of revision:
Handle: RePEc:cda:wpaper:03-3

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Related research

Keywords: search; causality; structural vector autoregression; graph theory; common cause; causal Markov condition; Wold causal order; identification; PC algorithm;

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References

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  1. Christopher A. Sims, 1986. "Are forecasting models usable for policy analysis?," Quarterly Review, Federal Reserve Bank of Minneapolis, issue Win, pages 2-16.
  2. Leamer, Edward E., 1985. "Vector autoregressions for causal inference?," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 22(1), pages 255-304, January.
  3. Kevin Hoover & Stephen Perez, 2001. "Three attitudes towards data mining," Journal of Economic Methodology, Taylor and Francis Journals, vol. 7(2), pages 195-210.
  4. Hoover, Kevin, 2000. "Truth and Robustness in Cross-Country Growth Regression," Working Papers 01-1, University of California at Davis, Department of Economics.
  5. 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.
  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. Kevin Hoover & Stephen J. Perez, 2003. "Data Mining Reconsidered: Encompassing And The General-To-Specific Approach To Specification Search," Working Papers 9727, University of California, Davis, Department of Economics.
  8. Hoover,Kevin D., 2001. "Causality in Macroeconomics," Cambridge Books, Cambridge University Press, number 9780521452175.
  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. 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.
  11. Steven M. Sheffrin & Robert K. Triest, 1995. "A new approach to causality and economic growth," Working Papers 95-12, Federal Reserve Bank of Boston.
  12. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
  13. Cooley, Thomas F. & Leroy, Stephen F., 1985. "Atheoretical macroeconometrics: A critique," Journal of Monetary Economics, Elsevier, vol. 16(3), pages 283-308, November.
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