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

Author

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  • Li, Yushu

    (CAFO, Växjö University)

  • Shukur, Ghazi

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

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.

Suggested Citation

  • Li, Yushu & Shukur, Ghazi, 2010. "Linear and Non-linear Causality Test in a LSTAR model - wavelet decomposition in a non-linear environment," Working Paper Series in Economics and Institutions of Innovation 227, Royal Institute of Technology, CESIS - Centre of Excellence for Science and Innovation Studies.
    • Handle: RePEc:hhs:cesisp:0227
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    References listed on IDEAS

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    1. Christoph Schleicher, 2002. "An Introduction to Wavelets for Economists," Staff Working Papers 02-3, Bank of Canada.
    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. Huh, Hyeon-seung, 2002. "GDP growth and the composite leading index: a nonlinear causality analysis for eleven countries," Economics Letters, Elsevier, vol. 77(1), pages 93-99, September.
    4. Li, Jing, 2006. "Testing Granger Causality in the presence of threshold effects," International Journal of Forecasting, Elsevier, vol. 22(4), pages 771-780.
    5. Hiemstra, Craig & Jones, Jonathan D, 1994. "Testing for Linear and Nonlinear Granger Causality in the Stock Price-Volume Relation," Journal of Finance, American Finance Association, vol. 49(5), pages 1639-1664, December.
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    Cited by:

    1. Prasad Bal, Debi & Narayan Rath, Badri, 2015. "Nonlinear causality between crude oil price and exchange rate: A comparative study of China and India," Energy Economics, Elsevier, vol. 51(C), pages 149-156.
    2. Yii Siing Wong & Chong Mun Ho & Brian Dollery, 2012. "Impact of exchange rate volatility on import flows: the case of Malaysia and the United States," Applied Financial Economics, Taylor & Francis Journals, vol. 22(24), pages 2027-2034, December.
    3. 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.
    4. Monica Billio & Silvio Di Sanzo, 2015. "Granger-causality in Markov switching models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(5), pages 956-966, May.

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    More about this item

    Keywords

    Granger causality; LSTAR model; Wavelet multiresolution; Monte Carlo simulation;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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