An Iterative Plug-In Algorithm for Nonparametric Modelling of Seasonal Time Series
AbstractThis paper focuses on developing a new data-driven procedure for decomposing seasonal time series based on local regression. Formula of the asymptotic optimal bandwidth hA in the current context is given. Methods for estimating the unknowns in hA are investigated. A data-driven algorithm for decomposing seasonal time series is proposed based on the iterative plug-in idea introduced by Gasser et al. (1991). Asymptotic behaviour of this algorithm is investigated. Some computational aspects are discussed in detail. Practical performance of the proposed algorithm is illustrated by simulated and data examples. The results here also provide some insights into the iterative plug-in idea.
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Bibliographic InfoPaper provided by Center of Finance and Econometrics, University of Konstanz in its series CoFE Discussion Paper with number 02-04.
Length: 27 pages
Date of creation: Feb 2002
Date of revision:
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- Jan Beran & Yuanhua Feng, 2002. "Local Polynomial Fitting with Long-Memory, Short-Memory and Antipersistent Errors," Annals of the Institute of Statistical Mathematics, Springer, vol. 54(2), pages 291-311, June.
- 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.
- Jan Beran & Yuanhua.Feng, 2001. "Iterative plug-in algorithms for SEMIFAR models - definition, convergence and asymptotic properties," CoFE Discussion Paper 01-11, Center of Finance and Econometrics, University of Konstanz.
- Jan Beran & Yuanhua.Feng, 2002. "Recent Developments in Non- and Semiparametric Regression with Fractional Time Series Errors," CoFE Discussion Paper 02-13, Center of Finance and Econometrics, University of Konstanz.
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