Model misspecification is a serious issue since misspecification generally renders statistical inference invalid. However, specification testing of discrete choice models is rarely applied. This paper describes a nonparametric test procedure which uses a combination of smoothed residual plots and a test statistic able to detect general misspecification. Nonparametric methods require large datasets when the number of independent variables is more than a few. A way to circumvent this problem is indicated, increasing the usefulness of the approach also with limited datasets.
discrete choice specification test nonparametric functional form