Wavelet Estimation of Gegenbauer Processes: Simulation and Empirical Application

Heni Boubaker ()

Computational Economics, 2015, vol. 46, issue 4, 574 pages

Abstract: The aim of this paper is to estimate the parameters of a stationary Gegenbauer process using a wavelet methodology where the selection of the orthonormal basis is given by generalized variance portmanteau test. Two other maximum likelihood estimators, including the Whittle and the wavelets—Whitcher (Technometrics 46:225–238, 2004 ) estimators, are also considered. We have shown by Monte-Carlo experiments that the new selection procedure improves considerably the Whittle and Whitcher estimators. Moreover, to assess the impact of volatility in the estimation methods, we assumed that the innovations $$\varepsilon _{t}$$ ε t are generated by univariate GARCH process. Simulation experiments show that the wavelets estimators perform better under most situations than the Whittle estimator. We then applied this new selection method to the consumer price index in monthly frequencies for the United States and find that this is more appropriate for forecasts. Copyright Springer Science+Business Media New York 2015

Keywords: Gegenbauer process; Wavelet analysis; Generalized variance portmanteau test; Heteroskedasticity; Monte-Carlo simulation; CPI; C13; C15; C22 (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s10614-014-9471-6

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