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A Bootstrap Test for Causality with Endogenous Lag Length Choice - theory and application in finance

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  • Hacker, R. Scott

    ()
    (CESIS - Centre of Excellence for Science and Innovation Studies, Royal Institute of Technology)

  • Hatemi-J, Abdulnasser

    ()
    (UAE University)

Abstract

Granger causality tests have become among the most popular empirical applications with time series data. Several new tests have been developed in the literature that can deal with different data generating processes. In all existing theoretical papers 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. This paper suggests 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. We provide and accordingly evaluate a Granger-causality bootstrap test which may be used with data that may or may not be integrated, and compare the performance of this test to that for the analogous asymptotic test. The suggested bootstrap test performs well and appears to be also robust to ARCH effects that usually characterize the financial data. This test is applied to testing the causal impact of the US financial market on the market of the United Arab Emirates.

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Bibliographic Info

Paper provided by Royal Institute of Technology, CESIS - Centre of Excellence for Science and Innovation Studies in its series Working Paper Series in Economics and Institutions of Innovation with number 223.

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Length: 21 pages
Date of creation: 10 Apr 2010
Date of revision:
Handle: RePEc:hhs:cesisp:0223

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Postal: CESIS - Centre of Excellence for Science and Innovation Studies, Royal Institute of Technology, SE-100 44 Stockholm, Sweden
Phone: +46 8 790 95 63
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Keywords: Causality; VAR Model; Stability; Endogenous Lag; ARCH; Leverages;

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  1. Granger, C. W. J., 1988. "Some recent development in a concept of causality," Journal of Econometrics, Elsevier, vol. 39(1-2), pages 199-211.
  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. 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.
  4. 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.
  5. 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.
  6. 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.
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Cited by:
  1. Selim Yildirim & Bilge Kagan Ă–zdemir & 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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