All Rights Reserved
AccessEcon LLC 2006, 2008.
Powered by MinhViet JSC

Haibin Wu
''Wavelet Estimation of Time Series Regression with Long Memory Processes''
( 2006, Vol. 3 No.33 )
This paper studies the estimation of time series regression when both regressors and disturbances have long memory. In contrast with the frequency domain estimation as in Robinson and Hidalgo (1997), we propose to estimate the same regression model with discrete wavelet transform (DWT) of the original series. Due to the approximate de-correlation property of DWT, the transformed series can be estimated using the traditional least squares techniques. We consider both the ordinary least squares and feasible generalized least squares estimator. Finite sample Monte Carlo simulation study is performed to examine the relative efficiency of the wavelet estimation.
Keywords: Discrete Wavelet Transform
C2 - Single Equation Models; Single Variables: General
Manuscript Received : Dec 06 2006 Manuscript Accepted : Dec 06 2006

  This abstract has been downloaded 2002 times                The Full PDF of this paper has been downloaded 153794 times