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 intrafamily component but require that observations from different families be independent. We establish consistency and asymptotic normality for our procedures. As usual, the rates of convergence can be very slow depending on the behavior of the characteristic function at infinity. We investigate the practical performance of our method in a simple Monte Carlo experiment.

JEL-codes: C1 (search for similar items in EconPapers)
Date: 2002-04
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Citations: View citations in EconPapers (11)

Published in Econometric Theory, April, 2002, 18(2), pp. 420-468. ISSN: 1469-4360

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http://eprints.lse.ac.uk/320/ Open access version. (application/pdf)

Related works:
Journal Article: NONPARAMETRIC ESTIMATION WITH AGGREGATED DATA (2002)
Working Paper: Nonparametric Estimation with Aggregated Data (2000)
Working Paper: Nonparametric estimation with aggregated data (2000)
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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:320

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