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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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
- 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.:
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- Scott Hacker & Abdulnasser Hatemi-J, 2010. "HHcte: GAUSS module to Apply a Bootstrap Test for Causality with Endogenous Lag Order," Statistical Software Components G00012, Boston College Department of Economics.
- 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.
- R. Scott Hacker & Abdulnasser Hatemi-J, 2006. "Tests for causality between integrated variables using asymptotic and bootstrap distributions: theory and application," Applied Economics, Taylor and 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.
- R. Scott Hacker & Abdulnasser Hatemi-J, 2005. "A test for multivariate ARCH effects," Applied Economics Letters, Taylor and Francis Journals, vol. 12(7), pages 411-417.
- Selim Yildirim & Bilge Kagan Özdemir & Burhan Dogan, 2013. "Financial Development and Economic Growth Nexus in Emerging European Economies: New Evidence from Asymmetric Causality," International Journal of Economics and Financial Issues, Econjournals, vol. 3(3), pages 710-722.
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