Estimating Long Memory Causality Relationships by a Wavelet Method

Arbeitspapier

The traditional causality relationship proposed by Granger (1969) assumes the relationships between variables are short range dependent with the same integrated order. Chen (2006) proposed a bi-variate model which can catch the long-range dependent among the two variables and the series do not need to be fractionally co-integrated. A long memory fractional transfer function is introduced to catch the long-range dependent in this model and a pseudo spectrum based method is proposed to estimate the long memory parameter in the bi-variate causality model. 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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Working Paper ; No. 2012:15

Classification and Topics

• Classification: Wirtschaft ; Multiple or Simultaneous Equation Models; Multiple Variables: General ; Model Construction and Estimation ; Computational Techniques; Simulation Modeling
• Subject: Granger causality ; long memory ; Monte Carlo simulation ; wavelet domain

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Li, Yushu
• Event: Veröffentlichung
• (who): Lund University, School of Economics and Management, Department of Economics
• (where): Lund
• (when): 2012

Further information

• Handle: http://hdl.handle.net/10419/260040
• Last update: 10.03.2025, 11:42 AM CET