We develop a method of testing linearity using power transforms of regressors, allowing for stationary processes and time trends. The linear model is a simplifying hypothesis that derives from the power transform model in three different ways, each producing its own identification problem. We...

We develop a method of testing linearity using power transforms of regressors, allowing for stationary processes and time trends. The linear model is a simplifying hypothesis that derives from the power transform model in three different ways, each producing its own identification problem. We...

We provide a new characterization of the equality of two positive-definite matrices A and B, and we use this to propose several new computationally convenient statistical tests for the equality of two unknown positive-definite matrices. Our primary focus is on testing the information matrix...

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...

Xiao (2009) develops a novel estimation technique for quantile cointegrated time series by extending Phillips and Hansen¡¯s (1990) semiparametric approach and Saikkonen¡¯s (1991) parametrically augmented approach. This paper extends Pesaran and Shin¡¯s (1998) autoregressive distributed-lag...

We provide mathematical proofs for the results in ¡°Testing for Neglected Nonlinearity Using Twofold Unidentified Models under the Null and Hexic Expansions¡± by Cho, Ishida, and White (2013).

This paper analyzes the interrelationships among Wald, likelihood ratio, Lagrange multiplier statistics for testing neglected nonlinearity. We show that the three test statistics are equivalent under the null although there exists a twofold identification problem. This implies that the trinity...

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...