Identifying significant model inputs with neural networks: Tax court determination of reasonable compensation

摘要

Neural networks have much to offer academic researchers and business practitioners. For example, recent research has shown that neural networks can classify and predict as well as traditional statistical methods, such as ordinary least squares (OLS). Neural networks, however, are limited in that they do not provide measures of significance of individual inputs as OLS (and other methods) provides. When neural networks overcome this limitation their variety and numbers of applications will increase dramatically and they will become more valuable to academe and practitioners. This study compares the abilities of OLS and neural networks, when used in conjunction with the Wilcoxen signed-ranks test to identify significant model inputs.