Recent Developments in Non- and Semiparametric Regression with Fractional Time Series Errors
This paper summarizes recent developments in non- and semiparametric regres- sion with stationary fractional time series errors, where the error process may be short-range, long-range dependent or antipersistent. The trend function in this model is estimated nonparametrically, while the dependence structure of the error process is estimated by approximate maximum likelihood. Asymptotic properties of these estimators are described briefly. The focus is on describing the developments of bandwidth selection in this context based on the iterative plug-in idea (Gasser et al., 1991) and some detailed computational aspects. Applications in the framework of the SEMIFAR (semiparametric fractional autoregressive) model (Beran, 1999) illustrate the practical usefulness of the methods described here.
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EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
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- Jan Beran & Yuanhua Feng & Siegfried Heiler, 2000. "Modifying the double smoothing bandwidth selector in nonparametric regression," CoFE Discussion Paper 00-37, Center of Finance and Econometrics, University of Konstanz.
- Ding, Zhuanxin & Granger, Clive W. J. & Engle, Robert F., 1993. "A long memory property of stock market returns and a new model," Journal of Empirical Finance, Elsevier, vol. 1(1), pages 83-106, June.
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- Yuanhua Feng, 2002. "An Iterative Plug-In Algorithm for Nonparametric Modelling of Seasonal Time Series," CoFE Discussion Paper 02-04, Center of Finance and Econometrics, University of Konstanz.
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