On Conditional Density Estimation
AbstractWith the aim to mitigate the possibleproblem of negativity in the estimation of the conditionaldensity function, we introduce a so-called re-weightedNadaraya-Watson (RNW) estimator. The proposed RNWestimator is constructed by a slight modificationof the well-known Nadaraya-Watson smoother.Because the estimator is explicitly defined in terms ofthe data, its practical implementation is quite simple.With a detailed asymptotic analysis, we demonstratethat the RNW smoother preserves thesuperior large-sample bias property of thelocal linear smoother of the conditional densityproposed by Fan, Yao and Tong (1996).As a matter of independent statistical interest,the limit distribution of the RNW estimator is alsoderived.
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Bibliographic InfoPaper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 02-032/4.
Date of creation: 08 Apr 2002
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alpha-mixing; asymptotic properties; negativity; nonparametric; re-weighted;
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- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
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- Holmes, Michael P. & Gray, Alexander G. & Isbell Jr., Charles Lee, 2010. "Fast kernel conditional density estimation: A dual-tree Monte Carlo approach," Computational Statistics & Data Analysis, Elsevier, vol. 54(7), pages 1707-1718, July.
- repec:ebl:ecbull:v:3:y:2007:i:62:p:1-6 is not listed on IDEAS
- Kim Huynh & David Jacho-Chavez, 2007. "Conditional density estimation: an application to the Ecuadorian manufacturing sector," Economics Bulletin, AccessEcon, vol. 3(62), pages 1-6.
- Manzan, Sebastiano & Zerom, Dawit, 2008. "A bootstrap-based non-parametric forecast density," International Journal of Forecasting, Elsevier, vol. 24(3), pages 535-550.
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