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A test for multimodality of regression derivatives with application to nonparametric growth regressions
Author(s) -
Henderson Daniel J.
Publication year - 2010
Publication title -
journal of applied econometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.878
H-Index - 99
eISSN - 1099-1255
pISSN - 0883-7252
DOI - 10.1002/jae.1099
Subject(s) - nonparametric statistics , econometrics , kernel density estimation , kernel (algebra) , nonparametric regression , regression , convergence (economics) , kernel regression , multimodality , mathematics , statistics , semiparametric regression , regression analysis , economics , computer science , combinatorics , estimator , world wide web , economic growth
This paper presents a method to test for multimodality of an estimated kernel density of derivative estimates from a nonparametric regression. The test is included in a study of nonparametric growth regressions. The results show that in the estimation of unconditional β‐convergence the distribution of the partial effects is multimodal, with one mode in the negative region (primarily OECD economies) and possibly two modes in the positive region (primarily non‐OECD economies) of the estimates. The results for conditional β‐convergence show that the density is predominantly negative and there is mixed evidence that the distribution is unimodal. Copyright © 2009 John Wiley & Sons, Ltd.