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  • Search: subject:"Distance and direction approach"
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Subject
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Artificial neural networks 1 Distance and direction approach 1 Gaussian stochastic process 1 Neglected nonlinearity 1 Quasi-likelihood ratio test 1 Sixth-order (hexic) expansion 1 Twofold identification problem 1 Weighted bootstrap 1
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Type of publication
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Book / Working Paper 1
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Undetermined 1
Author
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CHO, JIN SEO 1 ISHIDA, ISAO 1 WHITE, HALBERT 1
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Economic Research Institute, College of Business and Economics 1
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Working papers / Economic Research Institute, College of Business and Economics 1
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RePEc 1
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Testing for Neglected Nonlinearity Using Twofold Unidentified Models under the Null and Hexic Expansions (published in: Essays in Nonlinear Time Series Econometrics, Festschrift in Honor of Timo Terasvirta. Eds. Niels Haldrup, Mika Meitz, and Pentti Saikkonen (2014). Oxford: Oxford University Press.)
CHO, JIN SEO; ISHIDA, ISAO; WHITE, HALBERT - Economic Research Institute, College of Business and … - 2013
We revisit the twofold identification problem discussed by Cho, Ishida, and White (Neural Computation, 2011), which arises when testing for neglected nonlinearity by artificial neural networks. We do not use the so-called ¡°no-zero¡± condition and employ a sixth-order expansion to obtain the...
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