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k-NEAREST NEIGHBOR ESTIMATION OF INVERSE-DENSITY-WEIGHTED EXPECTATIONS WITH DEPENDENT DATA

Published online by Cambridge University Press:  21 February 2012

Ba Chu  and
David T. Jacho-Chávez
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Abstract

This paper considers the problem of estimating expected values of functions that are inversely weighted by an unknown density using the k-nearest neighbor (k-NN) method. It establishes the -consistency and the asymptotic normality of an estimator that allows for strictly stationary time-series data. The consistency of the Bartlett estimator of the derived asymptotic variance is also established. The proposed estimator is also shown to be asymptotically semiparametric efficient in the independent random sampling scheme. Monte Carlo experiments show that the proposed estimator performs well in finite sample applications.

Type
Research Article
Information
Econometric Theory , Volume 28 , Issue 4 , August 2012 , pp. 769 - 803
Copyright
Copyright © Cambridge University Press 2012

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Footnotes

The authors would like to thank the co-editor and three referees for their valuable comments that led to corrections and various improvements in the paper. Francesco Bravo and Juan Carlos Escanciano also provided many helpful comments, suggestions, and corrections. The authors acknowledge funding from the Social Science and Humanities Research Council of Canada (MBF Grant 410-2011-1700). The usual disclaimer applies.

References

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