Empirical likelihood inference for partially time-varying coefficient errors-in-variables models

Abstract

In this paper, the empirical likelihood inferences for partially time-varying coefficient errors-in-variables model with dependent observations are investigated. We propose an empirical log-likelihood ratio function for the regression parameters and show that its limiting distribution is a mixture of central chi-squared distributions. In order that the Wilks’ phenomenon holds, we construct an adjusted empirical log-likelihood ratio for the regression parameters. The adjusted empirical log-likelihood is shown to have a standard chi-squared limiting distribution. Simulations show that the proposed confidence regions have satisfactory performance.