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Uniform Convergence Rates for Kernel Estimation with Dependent Data

Bruce E. Hansen
Econometric Theory
Vol. 24, No. 3 (Jun., 2008), pp. 726-748
Stable URL: http://www.jstor.org/stable/20142515
Page Count: 23
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Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Uniform Convergence Rates for Kernel Estimation with Dependent Data
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Abstract

This paper presents a set of rate of uniform consistency results for kernel estimators of density functions and regressions functions. We generalize the existing literature by allowing for stationary strong mixing multivariate data with infinite support, kernels with unbounded support, and general bandwidth sequences. These results are useful for semiparametric estimation based on a first-stage nonparametric estimator.

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