  The empirical likelihood inference is extended to a class of semiparametric
models for stationary, weakly dependent series. A partially linear single-index
regression is used for the conditional mean of the series given its past, and
the present and past values of a vector of covariates. A parametric model for
the conditional variance of the series is added to capture further nonlinear
effects. We propose a fixed number of suitable moment equations which
characterize the mean and variance model. We derive an empirical log-likelihood
ratio which includes nonparametric estimators of several functions, and we show
that this ratio has the same limit as in the case where these functions are
known.
