Wavelet transform for regression estimation of non-stationary fractional time series
AbstractWe consider a semiparametric log periodogram regression estimation of memory parameter $d$ for non-stationary fractional time series using wavelet transformation. We propose wavelet-based log periodogram regression estimator, and obtain the asymptotic mean squared error, consistency and asymptotic normality of the estimator. The convergence rate of the mean squared error is the same as in the stationary case. Simulation studies show that wavelet-based estimator works reasonably well without using data differencing or data tapering, particularly when the short-run dependence is strong
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Bibliographic InfoPaper provided by Econometric Society in its series Econometric Society 2004 North American Summer Meetings with number 491.
Date of creation: 11 Aug 2004
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Non-stationary fractional process; Log periodogram regression; Wavelet transform;
Find related papers by JEL classification:
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
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- Jin Lee, 2004. "Wavelet transform for log periodogram regression in long memory stochastic volatility model," Econometric Society 2004 Far Eastern Meetings 682, Econometric Society.
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