Local rank tests in a multivariate nonparametric relationship

Consider a multivariate nonparametric model where the unknown vector of functions depends on two sets of explanatory variables. For a fixed level of one set of explanatory variables, we provide consistent statistical tests, called local rank tests, to determine whether the multivariate relationship can be explained by a smaller number of functions. We also provide estimators for the smallest number of functions, called local rank, explaining the relationship. The local rank tests and the estimators of the local rank are based on the asymptotics of the eigenvalues of some matrix. This matrix is estimated by using kernel-based methods and the asymptotics of its eigenvalues is established by using the so-called Fujikoshi expansions along with some techniques of the theory of U-statistics. We present a simulation study which examines small sample properties of local rank tests. We also apply the local rank tests and the local rank estimators of the paper to a demand system given by a newly constructed data set. Our results can be viewed as localized counterparts of tests for a number of factors in a nonparametric relationship introduced by Donald