Fast Computation and Bandwidth Selection Algorithms for Smoothing Functional Time Series*
Bastian Schäfer and
Yuanhua Feng ()
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Bastian Schäfer: Paderborn University
No 143, Working Papers CIE from Paderborn University, CIE Center for International Economics
This paper examines data-driven estimation of the mean surface in nonparamet- ric regression for huge functional time series. In this framework, we consider the use of the double conditional smoothing (DCS), an equivalent but much faster translation of the 2D-kernel regression. An even faster, but again equivalent func- tional DCS (FCDS) scheme and a boundary correction method for the DCS/FCDS is proposed. The asymptotically optimal bandwidths are obtained and selected by an IPI (iterative plug-in) algorithm. We show that the IPI algorithm works well in practice in a simulation study and apply the proposals to estimate the spot-volatility and trading volume surface in high-frequency nancial data under a functional representation. Our proposals also apply to large lattice spatial or spatial-temporal data from any research area.
Keywords: Spatial nonparametric regression; boundary correction; functional double conditional smoothing; bandwidth selection; spot volatility surface (search for similar items in EconPapers)
JEL-codes: C14 C51 (search for similar items in EconPapers)
Pages: 39 pages
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-isf, nep-mst and nep-ore
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Persistent link: https://EconPapers.repec.org/RePEc:pdn:ciepap:143
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