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Granger causality and the sampling of economic processes

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  • McCrorie, J. Roderick
  • Chambers, Marcus J.

Abstract

This paper provides a discussion of the developments in econometric modelling that are designed to deal with the problem of spurious Granger causality relationships that can arise from temporal aggregation.We outline the distortional e ects of using discrete time models that explicitly depend on the unit of time and outline a remedy of constructing timeinvariant discrete time models via a structural continuous time model.In an application to testing for money-income causality, we demonstrate the importance of incorporating exact temporal aggregation restrictions on the discrete time data.We do this by conducting causality tests in discrete time models that: (a) impose the temporal aggregation restrictions exactly; (b) impose the temporal aggregation restrictions approximately; and (c) do not impose these restrictions at all.
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  • McCrorie, J. Roderick & Chambers, Marcus J., 2006. "Granger causality and the sampling of economic processes," Journal of Econometrics, Elsevier, vol. 132(2), pages 311-336, June.
    • Handle: RePEc:eee:econom:v:132:y:2006:i:2:p:311-336
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    Cited by:

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    3. Chih-Nan Chen & Tsutomu Watanabe & Tomoyoshi Yabu, 2012. "A New Method for Identifying the Effects of Foreign Exchange Interventions," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 44(8), pages 1507-1533, December.
    4. Ghysels, Eric & Hill, Jonathan B. & Motegi, Kaiji, 2016. "Testing for Granger causality with mixed frequency data," Journal of Econometrics, Elsevier, vol. 192(1), pages 207-230.
    5. K. Lebedeva, 2015. "An Empirical Analysis of the Russian Financial Markets’ Liquidity and Returns," Review of Business and Economics Studies // Review of Business and Economics Studies, Финансовый Университет // Financial University, vol. 3(3), pages 5-31.
    6. Alessandro Gregorio & Francesco Iafrate, 2021. "Regularized bridge-type estimation with multiple penalties," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 73(5), pages 921-951, October.
    7. Chambers, MJ & McCrorie, JR & Thornton, MA, 2017. "Continuous Time Modelling Based on an Exact Discrete Time Representation," Economics Discussion Papers 20497, University of Essex, Department of Economics.
    8. 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.
    9. Salamaliki, Paraskevi K. & Venetis, Ioannis A., 2013. "Energy consumption and real GDP in G-7: Multi-horizon causality testing in the presence of capital stock," Energy Economics, Elsevier, vol. 39(C), pages 108-121.
    10. Michael A. Thornton & Marcus J. Chambers, 2013. "Temporal aggregation in macroeconomics," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 13, pages 289-310, Edward Elgar Publishing.
    11. Arie ten Cate, 2004. "Refinement of the partial adjustment model using continuous-time econometrics," CPB Discussion Paper 41, CPB Netherlands Bureau for Economic Policy Analysis.
    12. Ghysels, Eric, 2016. "Macroeconomics and the reality of mixed frequency data," Journal of Econometrics, Elsevier, vol. 193(2), pages 294-314.
    13. Daniel Ventosa-Santaulària & José Eduardo Vera-Valdés, 2008. "Granger-Causality in the presence of structural breaks," Economics Bulletin, AccessEcon, vol. 3(61), pages 1-14.
    14. Ehlers, Stefan & Gürtler, Marc & Olboeter, Sven, 2010. "Financial crises and information transfer: An empirical analysis of the lead-lag relationship between equity and CDS iTraxx Indices," Working Papers IF34V1, Technische Universität Braunschweig, Institute of Finance.

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    • 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

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