Statistical inference using higher-order information
This paper presents a class of minimum contrast estimators for stochastic processes with possible long-range dependence based on the information on higher-order spectral densities. The results on consistency and asymptotic normality of the proposed estimators are provided.
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Volume (Year): 98 (2007)
Issue (Month): 4 (April)
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References listed on IDEAS
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- Gao, Jiti & Anh, Vo & Heyde, Chris, 2002.
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- Gao, jiti & Anh, vo & Heyde, christopher, 1999. "Statistical estimation of nonstationaryGaussian processes with long-range dependence and intermittency," MPRA Paper 11972, University Library of Munich, Germany, revised 23 Oct 2001.
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"Econometric analysis of realized volatility and its use in estimating stochastic volatility models,"
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- Valery Buldygin & Frederic Utzet & Vladimir Zaiats, 2004. "Asymptotic Normality of Cross-correlogram Estimates of the Response Function," Statistical Inference for Stochastic Processes, Springer, vol. 7(1), pages 1-34, March.
- Heyde, C. C. & Gay, R., 1993. "Smoothed periodogram asymptotics and estimation for processes and fields with possible long-range dependence," Stochastic Processes and their Applications, Elsevier, vol. 45(1), pages 169-182, March.
- Zhang, Hong-Ching & Shaman, Paul, 1991. "On the calculation of cumulants of estimators arising from a linear time series regression model," Journal of Multivariate Analysis, Elsevier, vol. 37(2), pages 135-150, May.
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