Summary: Since its introduction by \textit{A.B. Owen} [Biometrika 75, No. 2, 237--249 (1988; Zbl 0641.62032); Ann. Stat. 18, No. 1, 90--120 (1990; Zbl 0712.62040)], the empirical likelihood method has been extensively investigated and widely used to construct confidence regions and to test hypotheses in the literature. For a large class of statistics that can be obtained via solving estimating equations, the empirical likelihood function can be formulated from these estimating equations as proposed by \textit{J. Qin} and \textit{J. Lawless} [Ann. Stat. 22, No. 1, 300--325 (1994; Zbl 0799.62049)]. If only a small part of parameters is of interest, a profile empirical likelihood method has to be employed to construct confidence regions, which could be computationally costly. The authors propose a jackknife empirical likelihood method to overcome this computational burden. This proposed method is easy to implement and works well in practice.