Title: Empirical Likelihood for Linear Structural Equation Models with Dependent Errors,
Abstract: We consider linear structural equation models that are associated with mixed
graphs. The structural equations in these models only involve observed
variables, but their idiosyncratic error terms are allowed to be correlated and
non-Gaussian. We propose empirical likelihood (EL) procedures for inference,
and suggest several modifications, including a profile likelihood, in order to
improve tractability and performance of the resulting methods. Through
simulations, we show that when the error distributions are non-Gaussian, the
use of EL and the proposed modifications may increase statistical efficiency
and improve assessment of significance.