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A bootstrap test for causality with endogenous lag length choice: theory and application in finance

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  • Scott Hacker
  • Abdulnasser Hatemi‐J

Abstract

Purpose - In all existing theoretical papers on causality it is assumed that the lag length is known a priori. However, in applied research the lag length has to be selected before testing for causality. The purpose of this paper is to suggest that in investigating the effectiveness of various Granger causality testing methodologies, including those using bootstrapping, the lag length choice should be endogenized, by which we mean the data‐driven preselection of lag length should be taken into account. Design/methodology/approach - The size and power of a bootstrap test with endogenized lag‐length choice are investigated by simulation methods. A statistical software component is produced to implement the test, which is available online. Findings - The simulation results show that this test performs well. An application of the test provides empirical support for the hypothesis that the UAE financial market is integrated with the US market. Social implications - The empirical results based on this test are expected to be more precise. Originality/value - This paper considers a bootstrap test for causality with endogenous lag order. This test has superior properties compared to existing causality tests in terms of size, with similar if not better power and it is robust to ARCH effects that usually characterize financial data. Practitioners interested in causal inference based on time series data might find the test valuable.

Suggested Citation

  • Scott Hacker & Abdulnasser Hatemi‐J, 2012. "A bootstrap test for causality with endogenous lag length choice: theory and application in finance," Journal of Economic Studies, Emerald Group Publishing Limited, vol. 39(2), pages 144-160, May.
    • Handle: RePEc:eme:jespps:v:39:y:2012:i:2:p:144-160
    DOI: 10.1108/01443581211222635
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    References listed on IDEAS

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    1. Toda, Hiro Y. & Yamamoto, Taku, 1995. "Statistical inference in vector autoregressions with possibly integrated processes," Journal of Econometrics, Elsevier, vol. 66(1-2), pages 225-250.
    2. R. Scott Hacker & Abdulnasser Hatemi-J, 2005. "A test for multivariate ARCH effects," Applied Economics Letters, Taylor & Francis Journals, vol. 12(7), pages 411-417.
    3. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    4. Granger, C. W. J., 1988. "Some recent development in a concept of causality," Journal of Econometrics, Elsevier, vol. 39(1-2), pages 199-211.
    5. R. Scott Hacker & Abdulnasser Hatemi-J, 2006. "Tests for causality between integrated variables using asymptotic and bootstrap distributions: theory and application," Applied Economics, Taylor & Francis Journals, vol. 38(13), pages 1489-1500.
    6. Hatemi-J, Abdulnasser, 2004. "Multivariate tests for autocorrelation in the stable and unstable VAR models," Economic Modelling, Elsevier, vol. 21(4), pages 661-683, July.
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    More about this item

    Keywords

    Causality; VAR model; Stability; Endogenous lag; ARCH; Leverages; Bootstrap; Economic stability; Finance;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • 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
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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