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Searching for the Causal Structure of a Vector Autoregression

Author

Listed:
  • 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.

Suggested Citation

  • Kevin Hoover & Selva Demiralp, 2003. "Searching for the Causal Structure of a Vector Autoregression," Working Papers 58, University of California, Davis, Department of Economics.
    • Handle: RePEc:cda:wpaper:58
    as

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    File URL: https://repec.dss.ucdavis.edu/files/7aK4eecCA7ZAECJMTWwSdrA5/03-3.pdf
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    References listed on IDEAS

    as
    1. Kevin D. Hoover & Stephen J. Perez, 1999. "Data mining reconsidered: encompassing and the general-to-specific approach to specification search," Econometrics Journal, Royal Economic Society, vol. 2(2), pages 167-191.
    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. Hoover,Kevin D., 2001. "Causality in Macroeconomics," Cambridge Books, Cambridge University Press, number 9780521002882, September.
    4. Brunner, Karl & Meltzer, Allan H., 1985. "Understanding monetary regimes," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 22(1), pages 1-8, January.
    5. 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.
    6. Kevin D. Hoover & Steven M. Sheffrin (ed.), 1995. "Monetarism And The Methodology Of Economics," Books, Edward Elgar Publishing, number 232.
    7. Cooley, Thomas F. & Leroy, Stephen F., 1985. "Atheoretical macroeconometrics: A critique," Journal of Monetary Economics, Elsevier, vol. 16(3), pages 283-308, November.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    search; causality; structural vector autoregression; graph theory; common cause; causal Markov condition; Wold causal order; identification; PC algorithm;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C49 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Other
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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