Electronic copy available at: http://ssrn.com/abstract=1677747 1 A New Diagnostic Test for Cross–Section Uncorrelatedness in Nonparametric Panel Data Models Jia Chen 1 , Jiti Gao and Degui Li The University of Adelaide, SA 5005, Australia Abstract In this paper, we propose a new diagnostic test for residual cross–section un- correlatedness in a nonparametric panel data model. The proposed nonparametric cross–section uncorrelatedness (CU) test is a nonparametric counterpart of an ex- isting parametric cross–section dependence (CD) test proposed in Pesaran (2004) for the parametric case. We establish asymptotic distributions of the proposed test statistic for several different cases. Without assuming cross–section independence, we establish asymptotic distributions for the proposed test for the case where both the cross–section dimension and the time dimension go to infinity simultaneously. We then analyze the power function of the proposed test under a sequence of local alternatives that involve a nonlinear multi–factor model. We also provide several numerical examples. The small sample studies show that the nonparametric CU test associated with an asymptotic critical value works well numerically in each individual case. An empirical analysis of a set of CPI data in Australian capital cities is given to examine the applicability of the proposed nonparametric CU test. Keywords: Cross–section uncorrelatedness; local linear smoother; nonlinear panel data model; nonparametric diagnostic test, size and power function 1 Jia Chen is from the School of Economics, The University of Adelaide. Adelaide SA 5005, Australia. Email: jia.chen@adelaide.edu.au.