The New Palgrave Dictionary of Economics

2018 Edition
| Editors: Macmillan Publishers Ltd

Granger–Sims Causality

  • G. M. Kuersteiner
Reference work entry


The concept of Granger–Sims causality is discussed in its historical context. There follows a review of the subsequent literature that explored conditions under which the definitions of Granger and Sims are equivalent. The relationship to the potential outcomes framework is explored in light of recent developments in the literature.


Block recursive structure Causality in economics and econometrics Conditional independence Conditional probability Covariance stationary processes Equivalence relationships Granger non-causality Granger, C. Granger–Sims causality Hume, D. Impulse response analysis Mill, J. S. Monetary policy rules Observational studies Potential outcomes Prediction error variance Rubin causal model Simon, H. Sims non-causality Structural innovations Structural vector autoregressions White noise 

JEL Classifications

This is a preview of subscription content, log in to check access.


  1. Angrist, J. and G. Kuersteiner. 2004. Semiparametric causality tests using the policy propensity score. Working paper no. 10975. Cambridge, MA: NBER.Google Scholar
  2. Basmann, R. 1963. The causal interpretation of non-triangular systems of economic relations. Econometrica 31: 439–448.CrossRefGoogle Scholar
  3. Bouissou, M., J.-J. Laffont, and Q. Voung. 1986. Tests of noncausality under Markov assumptions for qualitative panel data. Econometrica 54: 395–414.CrossRefGoogle Scholar
  4. Chamberlain, G. 1982. The general equivalence of Granger and Sims causality. Econometrica 50: 569–581.CrossRefGoogle Scholar
  5. Dawid, A. 1979. Conditional independence in statistical theory. Journal of the Royal Statistical Society, Series B 41: 1–31.Google Scholar
  6. Dufour, J.-M., and E. Renault. 1998. Short run and long run causality in time series: theory. Econometrica 66: 1099–1125.CrossRefGoogle Scholar
  7. Dufour, J.-M., and D. Tessier. 1993. On the relationship between impulse response analysis, innovation accounting and Granger causality. Economics Letters 42: 327–333.CrossRefGoogle Scholar
  8. Engle, R., D. Hendry, and J. Richard. 1983. Exogeneity. Econometrica 51: 277–304.CrossRefGoogle Scholar
  9. Feigl, H. 1953. Notes on causality. In Readings in the philosophy of science, ed. H. Feigl and M. Brodbeck. New York: Appleton-Century-Crofts, Inc..Google Scholar
  10. Florens, J.-P. 2003. Some technical issues in defining causality. Journal of Econometrics 112: 127–128.CrossRefGoogle Scholar
  11. Florens, J.-P., and M. Mouchart. 1982. A note on noncausality. Econometrica 50: 583–591.CrossRefGoogle Scholar
  12. Florens, J.-P., and M. Mouchart. 1985. A linear theory for noncausality. Econometrica 53: 157–176.CrossRefGoogle Scholar
  13. Granger, C. 1963. Economic processes involving feedback. Information and Control 6: 28–48.CrossRefGoogle Scholar
  14. Granger, C. 1969. Investigating causal relations by econometric models and crossspectral methods. Econometrica 37: 424–438.CrossRefGoogle Scholar
  15. Granger, C. 1980. Tests for causation – a personal viewpoint. Journal of Economic Dynamics and Control 2: 329–352.CrossRefGoogle Scholar
  16. Haavelmo, T. 1944. The probability approach in econometrics. Econometrica 12 (Suppl): iii–vi, 1–115.Google Scholar
  17. Holland, P. 1986. Statistics and causal inference. Journal of the American Statistical Association 81: 945–960.CrossRefGoogle Scholar
  18. Hoover, K. 2001. Causality in macroeconomics. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  19. Hoover, K., and S. Perez. 1994. Post hoc ergo propter once more: An evaluation of ‘does monetary policy matter?’ in the spirit of James Tobin. Journal of Monetary Economics 34: 47–73.CrossRefGoogle Scholar
  20. Hosoya, Y. 1977. On the Granger condition for non-causality. Econometrica 45: 1735–1736.CrossRefGoogle Scholar
  21. Jorda, O. 2005. Estimation and inference of impulse responses by local projections. American Economic Review 95: 162–182.CrossRefGoogle Scholar
  22. Leamer, E. 1985. Vector autoregressions for causal inference? Carnegie-Rochester Conference Series on Public Policy 22: 255–303.CrossRefGoogle Scholar
  23. Leeper, E. 1997. Narrative and VAR approaches to monetary policy: Common identification problems. Journal of Monetary Economics 40: 641–657.CrossRefGoogle Scholar
  24. Lütkepohl, H. 1993. Testing for causation between two variables in higher dimensional VAR models. In Studies in applied econometrics, ed. H. Schneeweiss and K. Zimmerman. Heidelberg: Springer-Verlag.Google Scholar
  25. McCallum, B. 1984. A linearized version of Lucas’s neutrality model. Canadian Journal of Economics 17: 138–145.CrossRefGoogle Scholar
  26. Orcutt, G. 1952. Actions, consequences, and causal relations. The Review of Economics and Statistics 34: 305–313.CrossRefGoogle Scholar
  27. Pearl, J. 2000. Causality. Cambridge: Cambridge University Press.Google Scholar
  28. Pierce, D., and L. Haugh. 1977. Causality in temporal systems. Journal of Econometrics 5: 265–293.CrossRefGoogle Scholar
  29. Robins, J., S. Greenland, and F. Hu. 1999. Estimation of the causal effect of a time-varying exposure on the marginal mean of a repeated binary outcome. Journal of the American Statistical Association 94: 687–700.CrossRefGoogle Scholar
  30. Romer, C., and D. Romer. 1989. Does monetary policy matter? A new test in the spirit of Friedman and Schwartz. In NBER macroeconomics annual 1989, ed. O. Blanchard and S. Fischer. Cambridge, MA: MIT Press.Google Scholar
  31. Rubin, D. 1974. Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology 66: 688–701.CrossRefGoogle Scholar
  32. Rubin, D. 1980. Randomization analysis of experimental data: the Fisher randomization test. Comment. Journal of the American Statistical Association 75: 591–593.Google Scholar
  33. Rudebusch, G., and L. Svensson. 1999. Policy rules for inflation targeting. In Monetary policy rules, ed. J. Taylor. Chicago: University of Chicago Press.Google Scholar
  34. Shapiro, M. 1994. Federal Reserve policy: cause and effect. In Monetary policy, ed. G. Mankiew. Chicago: University of Chicago Press.Google Scholar
  35. Simon, H. 1953. Causal ordering and identifiability. In Studies in econometric method, ed. W. Hood, T. Koopmans, and Cowles Commission Monograph No. 14. New York: John Wiley.Google Scholar
  36. Sims, C. 1972. Money, income and causality. American Economic Review 62: 540–552.Google Scholar
  37. Sims, C. 1977. Exogeneity and causal ordering in macroeconomic models. In New methods in business cycle research: Proceedings of a conference, ed. C. Sims. Minneapolis: Federal Reserve Bank of Minneapolis.Google Scholar
  38. Sims, C. 1979. A comment on the papers by Zellner and Schwert. Carnegie-Rochester Conference Series on Public Policy 10: 103–108.CrossRefGoogle Scholar
  39. Sims, C. 1980. Macroeconomics and reality. Econometrica 48: 1–48.CrossRefGoogle Scholar
  40. Sims, C. 1986. Are forecasting models usable for policy analysis? Federal Reserve Bank of Minneapolis Quarterly Review 10(1): 2–16.Google Scholar
  41. Strotz, R., and H. Wold. 1960. Recursive versus nonrecursive systems: an attempt at synthesis. Econometrica 28: 417–427.CrossRefGoogle Scholar
  42. Suppes, P. 1970. A probabilistic theory of causality. Amsterdam: North-Holland.Google Scholar
  43. White, H. 2006. Time-series estimation of the effects of natural experiments. Journal of Econometrics 135: 527–566.CrossRefGoogle Scholar
  44. Wiener, N. 1956. The theory of prediction. In Modern mathematics for the engineer, series 1, ed. E. Beckenback. New York: McGraw-Hill.Google Scholar
  45. Wold, H. 1954. Causality and econometrics. Econometrica 22: 162–177.CrossRefGoogle Scholar
  46. Zellner, A. 1979. Causality and econometrics, policy and policy making. Carnegie-Rochester Conference Series on Public Policy 10: 9–54.CrossRefGoogle Scholar

Copyright information

© Macmillan Publishers Ltd. 2018

Authors and Affiliations

  • G. M. Kuersteiner
    • 1
  1. 1.