Nonparametric Estimation and Symmetry Tests for Conditional Density Functions

Rob Hyndman and Q. Yao

No 17/98, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: We suggest two new methods for conditional density estimation. The first is based on locally fitting 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: TESTING; STATISTICAL ANALYSIS; ESTIMATION OF PARAMETERS (search for similar items in EconPapers)
JEL-codes: C13 C14 (search for similar items in EconPapers)
Pages: 15 pages
Date: 1998
New Economics Papers: this item is included in nep-ets
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Citations: View citations in EconPapers (23)

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Working Paper: Nonparametric estimation and symmetry tests for conditional density functions (2002)
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