Testing for Neglected Nonlinearity Using Extreme Learning Machines (published in: International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 21, Suppl. 2 (2013), 117--129.)

Kyu Lee Shin and Jin Seo Cho ()
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Kyu Lee Shin: Educational Research Institute, Inha University

No 2013rwp-57, Working papers from Yonsei University, Yonsei Economics Research Institute

Abstract: In this study, we introduce statistics for testing neglected nonlinearity using the extreme leaning machines introduced by Huang, Zhu, and Siew (2006, Neurocomputing) and call them ELMNN tests. The ELMNN tests are very convenient and can be widely applied because they are obtained as byproducts of estimating linear models, and they can serve as quick diagnostic test statistics complementing the computational burdens of other tests. For the proposed test statistics, we provide a set of regularity conditions under which they asymptotically follow a chi-squared distribution under the null and are consistent under the alternative. We conduct Monte Carlo experiments and examine how they behave when the sample size is finite. Our experiment shows that the tests exhibit the properties desired by the theory of this paper.

Keywords: Extreme learning machines; neglected nonlinearity; Wald test; single layer feedforward network; asymptotic distribution (search for similar items in EconPapers)
Pages: 35pages
Date: 2013-06
New Economics Papers: this item is included in nep-ecm
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