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Linear and Non-linear Causality Test in a LSTAR model - wavelet decomposition in a non-linear environment

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Author Info

  • Li, Yushu

    (CAFO, Växjö University)

  • Shukur, Ghazi

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

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Abstract

In this paper, we use simulated data to investigate the power of different causality tests in a two-dimensional vector autoregressive (VAR) model. The data are presented in a non-linear environment that is modelled using a logistic smooth transition autoregressive (LSTAR) function. We use both linear and non-linear causality tests to investigate the unidirection causality relationship and compare the power of these tests. The linear test is the commonly used Granger causality test. The non-linear test is a non-parametric test based on Baek and Brock (1992) and Hiemstra and Jones (1994). When implementing the non-linear test, we use separately the original data, the linear VAR filtered residuals, and the wavelet decomposed series based on wavelet multiresolution analysis (MRA). The VAR filtered residuals and the wavelet decomposition series are used to extract the non-linear structure of the original data. The simulation results show that the non-parametric test based on the wavelet decomposition series (which is a model free approach) has the highest power to explore the causality relationship in the non-linear models.

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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 227.

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Length: 17 pages
Date of creation: 10 Apr 2010
Date of revision:
Handle: RePEc:hhs:cesisp:0227

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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
Web page: http://www.infra.kth.se/cesis/
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Related research

Keywords: Granger causality; LSTAR model; Wavelet multiresolution; Monte Carlo simulation;

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  1. Li, Jing, 2006. "Testing Granger Causality in the presence of threshold effects," International Journal of Forecasting, Elsevier, vol. 22(4), pages 771-780.
  2. Bell, David & Kay, Jim & Malley, Jim, 1996. "A non-parametric approach to non-linear causality testing," Economics Letters, Elsevier, vol. 51(1), pages 7-18, April.
  3. Christoph Schleicher, 2002. "An Introduction to Wavelets for Economists," Working Papers 02-3, Bank of Canada.
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Cited by:
  1. Lim, Shiok Ye & Ho, Chong Mun, 2013. "Nonlinearity in ASEAN-5 export-led growth model: Empirical evidence from nonparametric approach," Economic Modelling, Elsevier, vol. 32(C), pages 136-145.

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