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Wavelet Estimation of Time Series Regression with Long Memory Processes

Author

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  • Haibin Wu

    (University of Alberta)

Abstract

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.

Suggested Citation

  • Haibin Wu, 2006. "Wavelet Estimation of Time Series Regression with Long Memory Processes," Economics Bulletin, AccessEcon, vol. 3(33), pages 1-10.
    • Handle: RePEc:ebl:ecbull:eb-05c40004
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    Cited by:

    1. Ibrahim M. Awad & Abdel-Rahman Al-Ewesat, 2017. "Volatility Persistence in Palestine Exchange Bulls and Bears: An Econometric Analysis of Time Series Data," Review of Economics & Finance, Better Advances Press, Canada, vol. 9, pages 83-97, August.
    2. repec:dau:papers:123456789/6515 is not listed on IDEAS

    More about this item

    Keywords

    Discrete Wavelet Transform;

    JEL classification:

    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables

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