Various nonparametric kernel regression estimators are presented, based on which we consider two nonparametric tests for neglected nonlinearity in time series regression models. One of them is the goodness-of-fit test of Cai, Fan, and Yao (2000) and another is the nonparametric conditional moment test by Li and Wang (1998) and Zheng (1996). Bootstrap procedures are used for these tests and thei...

Standard goodness-of-fit tests for a parametric regression model against a series of nonparametric alternatives are based on residuals arising from a fitted model.When a parametric regression model is compared with a nonparametric model, goodness-of-fit testing can be naturally approached by evaluating the likelihood of the parametric model within a nonparametric framework. We employ the empiri...

Glossary Hypothesis A hypothesis is a statement concerning the (joint) distribution underlying the observed data. Nonparametric test In contrast to a parametric test, a nonparametric test does not presume a particular parametric structure concerning the data generating process. Serial dependence Statistical dependence among time series observations.

In this paper, we propose a new diagnostic test for residual cross–section uncorrelatedness in a nonparametric panel data model. The proposed nonparametric cross– section uncorrelatedness (CU) test is a nonparametric counterpart of an existing parametric cross–section dependence (CD) test proposed in Pesaran (2004) for the parametric case. We establish asymptotic distributions of the proposed t...

In this paper, we consider a nonparametric permutation test on the correlation coefficient, which is applied to a significance test on regression coefficients. Because the permutation test is very computerintensive, there are few studies on small-sample properties, although we have numerous studies on asymptotic properties with regard to various aspects. In this paper, we aim to compare the per...