A Structural Approach for Testing Causality
The ever present possibility of confounding factors creates difficulties in identifying causal effects on the basis of observational data. A large number of approaches to resolve this difficulty have been proposed; see Zaman (2010) for a recent survey. One involves using a "natural experiment" where nature acts like an experimenter in changing the setting of a key variable, allowing us to differentiate between "treatment" and "control" observations. This idea has been used by Hendry and Ericsson (1991), Hoover (2001), and Keane (2010) in rather complex settings. This paper presents an elementary version of this structural approach for detecting causality in the simplest possible setting. The structural method is able to detect contemporaneous causality. We illustrate the uses of this technique on a simulated data set, and also apply it to the export-led growth hypothesis for India and energy-growth data for Shanghai.
Volume (Year): 3 (2011)
Issue (Month): 2 (September)
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References listed on IDEAS
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- Zaman, Asad, 2008.
"Causal Relations via Econometrics,"
10128, University Library of Munich, Germany.
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Cowles Foundation Discussion Papers
943, Cowles Foundation for Research in Economics, Yale University.
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- repec:eme:jespps:v:32:y:2005:i:2:p:132-145 is not listed on IDEAS
- Jim Love & Ramesh Chandra, 2004. "Testing Export-Led Growth in India, Pakistan and Sri Lanka Using a Multivariate Framework," Manchester School, University of Manchester, vol. 72(4), pages 483-496, 07.
- Rust, John, 2010. "Comments on: "Structural vs. atheoretic approaches to econometrics" by Michael Keane," Journal of Econometrics, Elsevier, vol. 156(1), pages 21-24, May.
- Wolde-Rufael, Yemane, 2004. "Disaggregated industrial energy consumption and GDP: the case of Shanghai, 1952-1999," Energy Economics, Elsevier, vol. 26(1), pages 69-75, January.
- Hoover, Kevin D., 2004. "Lost Causes," Journal of the History of Economic Thought, Cambridge University Press, vol. 26(02), pages 149-164, June.
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