Expansions for approximate maximum likelihood estimators of the fractional difference parameter
Abstract. Copyright 2005 Royal Economic Society
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Bibliographic InfoArticle provided by Royal Economic Society in its journal The Econometrics Journal.
Volume (Year): 8 (2005)
Issue (Month): 3 (December)
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Other versions of this item:
- Offer Lieberman & Peter C.B. Phillips, 2004. "Expansions for Approximate Maximum Likelihood Estimators of the Fractional Difference Parameter," Cowles Foundation Discussion Papers 1474, Cowles Foundation for Research in Economics, Yale University.
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
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- Offer Lieberman & Peter C.B. Phillips, 2006.
"A Complete Asymptotic Series for the Autocovariance Function of a Long Memory Process,"
Cowles Foundation Discussion Papers
1586, Cowles Foundation for Research in Economics, Yale University.
- Lieberman, Offer & Phillips, Peter C.B., 2008. "A complete asymptotic series for the autocovariance function of a long memory process," Journal of Econometrics, Elsevier, vol. 147(1), pages 99-103, November.
- Offer Lieberman & Peter C. B. Phillips, 2006.
"Refined Inference on Long Memory in Realized Volatility,"
Cowles Foundation Discussion Papers
1549, Cowles Foundation for Research in Economics, Yale University.
- Offer Lieberman & Peter Phillips, 2008. "Refined Inference on Long Memory in Realized Volatility," Econometric Reviews, Taylor & Francis Journals, vol. 27(1-3), pages 254-267.
- Gao, Jiti, 2007. "Nonlinear time series: semiparametric and nonparametric methods," MPRA Paper 39563, University Library of Munich, Germany, revised 01 Sep 2007.
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