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The Relation of Different Concepts of Causality in Econometrics

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  • Michael Lechner

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Abstract

Granger and Sims non-causality (GSNC) are compared to non-causality based on concepts popular in the microeconometrics and programme evaluation literature (potential outcome non-causality, PONC). GSNC is defined as a set of restrictions on joint distributions of random variables with observable sample counterparts, whereas PONC combines restrictions on partially unobservable variables (potential outcomes) with different identifying assumptions that relate potential to observable outcomes. Based on a dynamic model of potential outcomes, we find that in general neither of the concepts implies each other without further assumptions. However, identifying assumptions of the sequential selection on observable type provide the link between those concepts, such that GSNC implies PONC, and vice versa.

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Paper provided by Department of Economics, University of St. Gallen in its series University of St. Gallen Department of Economics working paper series 2006 with number 2006-15.

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Length: 25 pages
Date of creation: Jun 2006
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Handle: RePEc:usg:dp2006:2006-15

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Keywords: Granger causality; Sims causality; Rubin causality; potential outcome model; dynamic treatments;

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  1. Chamberlain, Gary, 1982. "The General Equivalence of Granger and Sims Causality," Econometrica, Econometric Society, vol. 50(3), pages 569-81, May.
  2. Joshua D. Angrist & Guido M. Kuersteiner, 2004. "Semiparametric Causality Tests Using the Policy Propensity Score," NBER Working Papers 10975, National Bureau of Economic Research, Inc.
  3. Michael Lechner, 2006. "Matching Estimating of Dynamic Treatment Models: Some Practical Issues," University of St. Gallen Department of Economics working paper series 2006 2006-03, Department of Economics, University of St. Gallen.
  4. Sims, Christopher A, 1972. "Money, Income, and Causality," American Economic Review, American Economic Association, vol. 62(4), pages 540-52, September.
  5. Dufour, Jean-Marie & Tessier, David, 1993. "On the relationship between impulse response analysis, innovation accounting and Granger causality," Economics Letters, Elsevier, vol. 42(4), pages 327-333.
  6. James J. Heckman, 2000. "Causal Parameters And Policy Analysis In Economics: A Twentieth Century Retrospective," The Quarterly Journal of Economics, MIT Press, vol. 115(1), pages 45-97, February.
  7. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-38, July.
  8. Imbens, Guido W & Angrist, Joshua D, 1994. "Identification and Estimation of Local Average Treatment Effects," Econometrica, Econometric Society, vol. 62(2), pages 467-75, March.
  9. Florens, Jean-Pierre & Mouchart, Michel, 1985. "A Linear Theory for Noncausality," Econometrica, Econometric Society, vol. 53(1), pages 157-75, January.
  10. Hosoya, Yuzo, 1977. "On the Granger Condition for Non-Causality," Econometrica, Econometric Society, vol. 45(7), pages 1735-36, October.
  11. Dufour, J.M. & Renault, E., 1995. "Short-Run and Long-Rub Causality in Time Series: Theory," Cahiers de recherche 9538, Centre interuniversitaire de recherche en ├ęconomie quantitative, CIREQ.
  12. Florens, J P & Mouchart, M, 1982. "A Note on Noncausality," Econometrica, Econometric Society, vol. 50(3), pages 583-91, May.
  13. Heckman, James J & Smith, Jeffrey, 1997. "Making the Most Out of Programme Evaluations and Social Experiments: Accounting for Heterogeneity in Programme Impacts," Review of Economic Studies, Wiley Blackwell, vol. 64(4), pages 487-535, October.
  14. Donald B. Rubin, 2005. "Causal Inference Using Potential Outcomes: Design, Modeling, Decisions," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 322-331, March.
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