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Nonparametric Estimation with Aggregated Data

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  • Oliver Linton
  • Yoon-Jae Whang

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

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File URL: http://sticerd.lse.ac.uk/dps/em/em397.pdf
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Bibliographic Info

Paper provided by Suntory and Toyota International Centres for Economics and Related Disciplines, LSE in its series STICERD - Econometrics Paper Series with number /2000/397.

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Date of creation: Jul 2000
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Handle: RePEc:cep:stiecm:/2000/397

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Web page: http://sticerd.lse.ac.uk/_new/publications/default.asp

Related research

Keywords: Aggregated data; deconvolution; grouped data; kernel; nonparametric regression;

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References

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  1. Donald W.K. Andrews & Yoon-Jae Whang, 1989. "Additive Interactive Regression Models: Circumvention of the Curse of Dimensionality," Cowles Foundation Discussion Papers 925, Cowles Foundation for Research in Economics, Yale University.
  2. Horowitz, Joel L & Markatou, Marianthi, 1996. "Semiparametric Estimation of Regression Models for Panel Data," Review of Economic Studies, Wiley Blackwell, vol. 63(1), pages 145-68, January.
  3. Robinson, Peter M, 1988. "Root- N-Consistent Semiparametric Regression," Econometrica, Econometric Society, vol. 56(4), pages 931-54, July.
  4. repec:cup:etheor:v:6:y:1990:i:4:p:466-79 is not listed on IDEAS
  5. Oliver Linton & Enno Mammen & N Nielsen, 2000. "The Existence and Asymptotic Properties of a Backfitting Projection Algorithm under Weak Conditions," STICERD - Econometrics Paper Series /2000/386, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
  6. Andrew Chesher, 1997. "Diet Revealed?: Semiparametric Estimation of Nutrient Intake-Age Relationships," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 160(3), pages 389-428.
  7. Masry, E., 1993. "Asymptotic Normality for Deconvolution Estimators of Multivariate Densities of Stationary Processes," Journal of Multivariate Analysis, Elsevier, vol. 44(1), pages 47-68, January.
  8. Linton, Oliver & Mammen, Enno & Nielsen, Jens Perch & Tanggaard, Carsten, 1998. "Estimating yield curves by Kernel smoothing methods," SFB 373 Discussion Papers 1999,54, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
  9. Oliver Linton & E. Mammen & J. Nielsen, 1999. "The existence and asymptotic properties of a backfitting projection algorithm under weak conditions," LSE Research Online Documents on Economics 300, London School of Economics and Political Science, LSE Library.
  10. Fan, Jianqing & Masry, Elias, 1992. "Multivariate regression estimation with errors-in-variables: Asymptotic normality for mixing processes," Journal of Multivariate Analysis, Elsevier, vol. 43(2), pages 237-271, November.
  11. repec:cup:etheor:v:13:y:1997:i:2:p:170-84 is not listed on IDEAS
  12. Knight, John L. & Satchell, Stephen E., 1997. "The Cumulant Generating Function Estimation Method," Econometric Theory, Cambridge University Press, vol. 13(02), pages 170-184, April.
  13. Joel L. Horowitz & Marianthi Markatou, 1993. "Semiparametric Estimation Of Regression Models For Panel Data," Econometrics 9309001, EconWPA.
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Cited by:
  1. Meister, Alexander, 2007. "Optimal convergence rates for density estimation from grouped data," Statistics & Probability Letters, Elsevier, vol. 77(11), pages 1091-1097, June.
  2. Li, Tong & Hsiao, Cheng, 2004. "Robust estimation of generalized linear models with measurement errors," Journal of Econometrics, Elsevier, vol. 118(1-2), pages 51-65.
  3. Xiaohong Chen & Yingyao Hu, 2006. "Identification and Inference of Nonlinear Models Using Two Samples with Arbitrary Measurement Errors," Cowles Foundation Discussion Papers 1590, Cowles Foundation for Research in Economics, Yale University.

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