A bootstrap test for causality with endogenous lag length choice: theory and application in finance
AbstractPurpose – 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. JEL classification: C32, C15, G11
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Bibliographic InfoArticle provided by Emerald Group Publishing in its journal Journal of Economic Studies.
Volume (Year): 39 (2012)
Issue (Month): 2 (May)
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Web page: http://www.emeraldinsight.com
Postal: Emerald Group Publishing, Howard House, Wagon Lane, Bingley, BD16 1WA, UK
Other versions of this item:
- Hacker, R. Scott & Hatemi-J, Abdulnasser, 2010. "A Bootstrap Test for Causality with Endogenous Lag Length Choice - theory and application in finance," Working Paper Series in Economics and Institutions of Innovation 223, Royal Institute of Technology, CESIS - Centre of Excellence for Science and Innovation Studies.
- 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
- G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
- 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.
- 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.
- 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.
- Granger, C. W. J., 1988. "Some recent development in a concept of causality," Journal of Econometrics, Elsevier, vol. 39(1-2), pages 199-211.
- 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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