Testing Serial Correlation in Partially Linear Single-Index Errors-in-Variables Models

Abstract

In this article, we propose two test statistics for testing the underlying serial correlation in a partially linear single-index model Y = η(Z τα) + X τβ + ϵ when X is measured with additive error. The proposed test statistics are shown to have asymptotic normal or chi-squared distributions under the null hypothesis of no serial correlation. Monte Carlo experiments are also conducted to illustrate the finite sample performance of the proposed test statistics. The simulation results confirm that these statistics perform satisfactorily in both estimated sizes and powers.