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- W2091963265 abstract "This paper discusses the estimation of regression and variance functions in nonparametric heteroscedastic regression models with long memory moving average errors and uniform nonrandom design on the unit interval. The consistency and the finite dimensional weak convergence of these estimators are established. For the regression function estimators, the asymptotic normality is established for all values of the long memory parameter 12<H<1; while for the variance function estimators, the asymptotic normality is proved for 12<H<34, nonnormality for 34<H<1. The paper also establishes the uniform convergence rate of the regression function estimators to be (nb)1-H/logn for all 12<H<1 and for a large class of innovations, including bounded and Gaussian innovations, where n is the series size and b is the bandwidth used in estimating the regression function. Additionally, the local Whittle estimator of H based on standardized nonparametric residuals is shown to be log(n)-consistent and the finite dimensional distributions of the studentized versions of the regression function estimators are shown to be asymptotically normal. These results thus generalize some of the results of Robinson to heteroscedastic regression models with long memory moving average errors." @default.
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- W2091963265 date "2007-02-01" @default.
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- W2091963265 title "Nonparametric regression with heteroscedastic long memory errors" @default.
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- W2091963265 doi "https://doi.org/10.1016/j.jspi.2006.01.016" @default.
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