Estimate Long Memory Causality Relationship by Wavelet Method

Yushu Li ()

Computational Economics, 2015, vol. 45, issue 4, 544 pages

Abstract: The traditional causality relationship proposed by Granger (Econometrica 37(3):424–438, 1969 ) assumes the relationships between variables are short range dependence with the same integrated order.Chen (J Forecast 25(3):193–200, 2006 , J Forecast 27:607–620, 2008 ) proposed a bivariate model which can catch the long-range dependence among the two variables and the series do not need to be fractionally co-integrated. A fractional integrated transfer function is introduced to catch the long-range dependence in this bivariate causality model and a pseudo spectrum based estimator is proposed to estimate the long memory parameter in the transfer function. In recent years, a wavelet domain-based method has gained popularity in estimations of long memory parameter in unit series. No extension to bi-series or multi-series has been made and this paper aims to fill this gap. We will construct an estimator for the long memory parameter in the bi-variable causality model in the wavelet domain. The theoretical background is derived and Monte Carlo simulation is used to investigate the performance of the estimator. Copyright Springer Science+Business Media New York 2015

Keywords: Granger causality; Long memory; Monte Carlo simulation; Wavelet domain; C22; C38; C63 (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1007/s10614-014-9434-y

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