Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/171879
Authors: 
Hirukawa, Masayuki
Sakudo, Mari
Year of Publication: 
2016
Citation: 
[Journal:] Econometrics [ISSN:] 2225-1146 [Volume:] 4 [Year:] 2016 [Issue:] 2 [Pages:] 1-27
Abstract: 
This paper improves a kernel-smoothed test of symmetry through combining it with a new class of asymmetric kernels called the generalized gamma kernels. It is demonstrated that the improved test statistic has a normal limit under the null of symmetry and is consistent under the alternative. A test-oriented smoothing parameter selection method is also proposed to implement the test. Monte Carlo simulations indicate superior finite-sample performance of the test statistic. It is worth emphasizing that the performance is grounded on the first-order normal limit and a small number of observations, despite a nonparametric convergence rate and a sample-splitting procedure of the test.
Subjects: 
asymmetric kernel
degenerate U-statistic
generalized gamma kernels
nonparametric kernel testing
smoothing parameter selection
symmetry test
two-sample goodness-of-fit test
JEL: 
C12
C14
Persistent Identifier of the first edition: 
Creative Commons License: 
http://creativecommons.org/licenses/by/4.0/
Document Type: 
Article
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