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File name: SSRN-id1144782. ; Size: 159K
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Uniform Convergence Rates of Kernel Estimators with Heterogenous, Dependent Data
Dennis Kristensen
University College London; University of Aarhus - CREATES; Cemmap (Centre for Microdata Methods and Practice)
June 10, 2008
Abstract:
The main uniform convergence results of Hansen (2008) are generalized in two directions: Data is allowed to (i) be heterogenously dependent and (ii) depend on a (possibly unbounded) parameter. These results are useful in semiparametric estimation problems involving time-inhomogenous models and/or sampling of continuous-time processes. The usefulness of these results are demonstrated by two applications: Kernel regression estimation of a time-varying AR(1) model, and the kernel density estimation of a Markov chain that has not been intialized at its stationary distribution.
Number of Pages in PDF File: 15
Keywords: Nonparametric estimation, uniform consistency, kernel estimation, density estimation, heterogeneous time series
JEL Classification: C14, C32
working papers series
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Date posted: June 16, 2008
Suggested CitationKristensen, Dennis, Uniform Convergence Rates of Kernel Estimators with Heterogenous, Dependent Data (June 10, 2008). Available at SSRN: http://ssrn.com/abstract=1144782 or http://dx.doi.org/10.2139/ssrn.1144782
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