By design a wavelet's strength rests in its ability to simultaneously localize a process in time-scale space. The wavelet's ability to localize a time series in time-scale space directly leads to the computational efficiency of the wavelet representation of a N X N matrix operator by allowing the N largest elements of the wavelet represented operator to adequately represent the matrix operator. This property allos many dense matrices to have sparse representation when transformed by wavelets. In this paper we generalize the long-memory parameter estimator of McCoy and Walden (1996) to simultaneously estaimte short and long-memory parameters. Using the sparse wavelet representation of a matrix operator, we are able to adequately approximate an ARFIMA models likelihood function with the series wavelet coefficients and their variances. Maximization of this approximate likelihood function over the short and long-memory parameter space results in the approximate wavelet maximum likelihood estimator of the ARFIMA model. By simultaneously maximizing the likelihood function over both the short and long-memory parameters, and using only the wavelet coefficient's variances, the approximate wavelet MLE provides an equally fast alternative to the frequency-domain MLE. Futhermore, the simulation studies reveal the approximate wavelet MLE to be robust over the invertible parameter region of the ARFIMA model's moving average parameter, whereas the frequency-domain MLE dramatically deteriorates as the moving average parameter approaches the boundaries of invertibility.
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Paper provided by EconWPA in its series Econometrics with number
9802003.
Length: 23 pages Date of creation: 16 Feb 1998 Date of revision:
21 Jun 1999 Handle: RePEc:wpa:wuwpem:9802003
Note: Type of Document - PostScript; prepared on LaTeX Sun Ultra 1; to print on PostScript; pages: 23 ; figures: included Contact details of provider: Web page: http://129.3.20.41
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Find related papers by JEL classification: C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics C5 - Mathematical and Quantitative Methods - - Econometric Modeling C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
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