Testing for neglected nonlinearity using artificial neural networks with many randomized hidden unit activations

DOI10.1515/jtse-2012-0021zbMath1462.62726OpenAlexW2116844554MaRDI QIDQ1695558

Zhou Xi, Ru Zhang, Tae-Hwy Lee

Publication date: 7 February 2018

Published in: Journal of Time Series Econometrics (Search for Journal in Brave)

Full work available at URL: https://economics.ucr.edu/repec/ucr/wpaper/201411.pdf

zbMATH Keywords

principal componentsBonferroni boundsmany activationsrandomized nuisance parameters

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Factor analysis and principal components; correspondence analysis (62H25) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Parametric hypothesis testing (62F03) Artificial neural networks and deep learning (68T07)

Related Items (1)

Testing for Neglected Nonlinearity Using Regularized Artificial Neural Networks

Cites Work

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• A Consistent Conditional Moment Test of Functional Form
• Asymptotic Theory of Integrated Conditional Moment Tests
• Inference When a Nuisance Parameter Is Not Identified Under the Null Hypothesis

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