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• W2036014214 abstract "Local linear fitting of nonlinear processes under strong (i.e., α-) mixing conditions has been investigated extensively. However, it is often a difficult step to establish the strong mixing of a nonlinear process composed of several parts such as the popular combination of autoregressive moving average (ARMA) and generalized autoregressive conditionally heteroskedastic (GARCH) models. In this paper we develop an asymptotic theory of local linear fitting for near epoch dependent (NED) processes. We establish the pointwise asymptotic normality of the local linear kernel estimators under some restrictions on the amount of dependence. Simulations and application examples illustrate that the proposed approach can work quite well for the medium size of economic time series.We thank Yuichi Kitamura and two referees for helpful comments. This research was partially supported by a Leverhulme Trust research grant, the National Natural Science Foundation of China, and the Economic and Social Science Research Council of the UK." @default.
• W2036014214 created "2016-06-24" @default.
• W2036014214 creator A5022214887 @default.
• W2036014214 creator A5022725373 @default.
• W2036014214 date "2006-12-06" @default.
• W2036014214 modified "2023-09-26" @default.
• W2036014214 title "LOCAL LINEAR FITTING UNDER NEAR EPOCH DEPENDENCE" @default.
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• W2036014214 doi "https://doi.org/10.1017/s0266466607070028" @default.
• W2036014214 hasPublicationYear "2006" @default.
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