Nonparametric estimation with aggregated data

Oliver Linton and Yoon-Jae Whang

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: We introduce a kernel-based estimator of the density function and regression function for data that have been grouped into family totals. We allow for a common intra-family component but require that observations from different families be in dependent. We establish consistency and asymptotic normality for our procedures. As usual, the rates of convergence can be very slow depending on the behaviour of the characteristic function at infinity. We investigate the practical performance of our method in a simple Monte Carlo experiment.

Keywords: Aggregated data; deconvolution; grouped data; kernel; nonparametric regression (search for similar items in EconPapers)
JEL-codes: C13 C14 C24 (search for similar items in EconPapers)
Pages: 50 pages
Date: 2000-07
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http://eprints.lse.ac.uk/2092/ Open access version. (application/pdf)

Related works:
Journal Article: NONPARAMETRIC ESTIMATION WITH AGGREGATED DATA (2002)
Working Paper: Nonparametric estimation with aggregated data (2002)
Working Paper: Nonparametric Estimation with Aggregated Data (2000)
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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:2092

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