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Testing for Granger causality in the presence of measurement errors

Author

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  • Jonas Andersson

    () (Norwegian School of Economics and Business Administration, Department of Finance and Management Science)

Abstract

In this paper a potential problem with tests for Granger-causality is investigated. If one of the two variables under study, but not the other, is measured with error the consequence is that tests of forecastablity of the variable without measurement error by the variable with measurement error will be rejected less often than it should. Since this is not the case for the test of forecastability of the variable with measurement error by the one without there is a danger of concluding that one variable leads the other while it is in fact a feed-back relationship. The problem is illustrated by an example.

Suggested Citation

  • Jonas Andersson, 2005. "Testing for Granger causality in the presence of measurement errors," Economics Bulletin, AccessEcon, vol. 3(47), pages 1-13.
  • Handle: RePEc:ebl:ecbull:eb-04c30003
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    Cited by:

    1. Bovi, Maurizio, 2013. "Are the representative agent’s beliefs based on efficient econometric models?," Journal of Economic Dynamics and Control, Elsevier, vol. 37(3), pages 633-648.
    2. Hoxha Adriatik, 2010. "Causal relationship between prices and wages: VECM analysis for Germany," EuroEconomica, Danubius University of Galati, issue 26, pages 90-106, November.
    3. 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.
    4. Fredrik N. G. Andersson & Katarzyna Burzynska & Sonja Opper, 2016. "Lending for growth? A Granger causality analysis of China’s finance–growth nexus," Empirical Economics, Springer, vol. 51(3), pages 897-920, November.

    More about this item

    Keywords

    Granger causality;

    JEL classification:

    • 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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