Nonparametric estimation and symmetry tests for conditional density functions
Qiwei Yao and
Rob Hyndman ()
LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library
We suggest two improved methods for conditional density estimation. The rst is based on locally tting a log-linear model, and is in the spirit of recent work on locally parametric techniques in density estimation. The second method is a constrained local polynomial estimator. Both methods always produce non-negative estimators. We propose an algorithm suitable for selecting the two bandwidths for either estimator. We also develop a new bootstrap test for the symmetry of conditional density functions. The proposed methods are illustrated by both simulation and application to a real data set.
Keywords: bandwidth selection; bootstrap; conditioning; density estimation; kernel smoothing; symmetry tests. (search for similar items in EconPapers)
JEL-codes: C1 (search for similar items in EconPapers)
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Published in Journal of Nonparametric Statistics, 2002, 14(3), pp. 259-278. ISSN: 1048-5252
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Working Paper: Nonparametric Estimation and Symmetry Tests for Conditional Density Functions (1998)
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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:6092
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