Bandwidth selection for the Local Polynomial Double Conditional Smoothing under Spatial ARMA Errors*

Bastian Schäfer
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Bastian Schäfer: Paderborn University

No 146, Working Papers CIE from Paderborn University, CIE Center for International Economics

Abstract: Nonparametric estimation of the mean surface of spatial data usually depends on a bivariate regressor, which is an ineffective estimation method for large data sets. The Double Conditional Smoothing (DCS) increases computational efficiency by reducing the regression problem to one dimension. We apply the DCS scheme to two-dimensional functional or spatial time series and use local polynomial regression for estimation of the regression surface and its derivatives. Asymptotic formulas for expectation and variance are given and formulas for the asymptotic optimal bandwidth derived. We propose a iterative plug-in algorithm for estimation of these optimal bandwidths under dependent errors. Spatial ARMA processes are used to model the error sequece parametrically and some estimation procedures for spatial ARMA processes are suggested. The proposed methods are assessed via a simulation study and applied to high-freqency financial data.

Keywords: Semiparametric regression; functional double conditional smoothing; bandwidth selection; iterative plug-in; dependent errors (search for similar items in EconPapers)
Pages: 45 pages
Date: 2021-11
New Economics Papers: this item is included in nep-ecm and nep-ore
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