The　estimation　and　empirical　likelihood　for　single-index　models　with　missing　covariates　are　studied.　A　generalized　estimating　equations　estimator　for　index　coefficients　with　missing　covariates　is　constructed,　and　its　asymptotic　distribution　is　obtained.　The　local　linear　estimator　for　link　function　achieves　optimal　convergence　rate.　By　using　the　bias-correction　and　inverse　selection　probability　weighted　methods,　a　class　of　empirical　likelihood　ratios　is　proposed　such　that　each　of　our　class　of　ratios　is　asymptotically　chi-squared.　A　simulation　study　indicates　that　the　proposed　methods　are　comparable　in　terms　of　coverage　probabilities　and　average　lengths　(areas)　of　confidence　intervals　(regions).　An　example　of　a　real　data　set　is　illustrated.　(C)　2013　Elsevier　B.V.　All　rights　reserved.