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A Test for Multimodality of Regression Derivatives with an Application to Nonparametric Growth Regressions

Listed author(s):
  • Henderson, Daniel J.

This paper presents a method to test for multimodality of an estimated kernel density of parameter estimates from a local-linear least-squares regression derivative. The procedure is laid out in seven simple steps and a suggestion for implementation is proposed. A Monte Carlo exercise is used to examine the finite sample properties of the test along with those from a calibrated version of it which corrects for the conservative nature of Silverman-type tests. The test is included in a study on nonparametric growth regressions. The results show that in the estimation of unconditional β-convergence, the distribution of the parameter estimates 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 parameter estimates. The results for conditional β-convergence show that the density is predominantly negative and unimodal. Finally, the application attempts to determine why particular observations posess positive marginal effects on initial income in both the unconditional and conditional frameworks.

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File URL: https://mpra.ub.uni-muenchen.de/8768/1/MPRA_paper_8768.pdf
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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 8768.

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Date of creation: 18 Apr 2008
Handle: RePEc:pra:mprapa:8768
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