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Noncausality in Continuous Time

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  • Florens, Jean-Pierre
  • Fougere, Denis

Abstract

Different concepts of noncausality for continuous time processes, using conditional independence and decomposition of semimartingales, are defined. As in the discrete-time setup, continuous time noncausality is a property concerned with the prediction horizon (global versus instantaneous noncausality) and the nature of the prediction (strong versus weak noncausality). Relations between the resulting continuous time noncausality concepts are then studied for the class of decomposable semimartingales for which, in general, the weak instantaneous noncausality does not imply the strong global noncausality. The paper then characterizes these different concepts in the case of counting processes and Markov processes. Copyright 1996 by The Econometric Society.

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Bibliographic Info

Article provided by Econometric Society in its journal Econometrica.

Volume (Year): 64 (1996)
Issue (Month): 5 (September)
Pages: 1195-1212

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Handle: RePEc:ecm:emetrp:v:64:y:1996:i:5:p:1195-1212

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Cited by:
  1. Taoufik Bouezmarni & Jeroen V.K. Rombouts & Abderrahim Taamouti, 2009. "A Nonparametric Copula Based Test for Conditional Independence with Applications to Granger Causality," Cahiers de recherche 0927, CIRPEE.
  2. Cherubini, Umberto & Mulinacci, Sabrina & Romagnoli, Silvia, 2011. "A copula-based model of speculative price dynamics in discrete time," Journal of Multivariate Analysis, Elsevier, vol. 102(6), pages 1047-1063, July.
  3. Petrovic, Ljiljana & Dimitrijevic, Sladjana, 2011. "Invariance of statistical causality under convergence," Statistics & Probability Letters, Elsevier, vol. 81(9), pages 1445-1448, September.
  4. Yacine Ait-Sahalia & Per A. Mykland, 2002. "The Effects of Random and Discrete Sampling When Estimating Continuous-Time Diffusions," NBER Technical Working Papers 0276, National Bureau of Economic Research, Inc.
  5. Van den Berg, Gerard J., 2001. "Duration models: specification, identification and multiple durations," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 55, pages 3381-3460 Elsevier.
  6. René Garcia & Éric Renault, 1998. "Risk Aversion, Intertemporal Substitution, and Option Pricing," CIRANO Working Papers 98s-02, CIRANO.
  7. White, Halbert, 2006. "Time-series estimation of the effects of natural experiments," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 527-566.
  8. Florens, Jean-Pierre, 2003. "Some technical issues in defining causality," Journal of Econometrics, Elsevier, vol. 112(1), pages 127-128, January.
  9. Oliver Linton & Pedro Gozalo, 1996. "Conditional Independence Restrictions: Testing and Estimation," Cowles Foundation Discussion Papers 1140, Cowles Foundation for Research in Economics, Yale University.
  10. McCrorie, J.R. & Chambers, M.J., 2004. "Granger Causality and the Sampling of Economic Processes," Discussion Paper 2004-39, Tilburg University, Center for Economic Research.
  11. Eichler, Michael & Didelez, Vanessa, 2009. "On Granger-causality and the effect of interventions in time series," Research Memorandum 003, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
  12. Renault, Eric & Werker, Bas J.M., 2011. "Causality effects in return volatility measures with random times," Journal of Econometrics, Elsevier, vol. 160(1), pages 272-279, January.
  13. Petrović, Ljiljana & Dimitrijević, Sladjana, 2012. "Causality with finite horizon of the past in continuous time," Statistics & Probability Letters, Elsevier, vol. 82(7), pages 1219-1223.

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