  Empirical likelihood method has been applied to dependent observations by
Monti (1997) through the Whittle's estimation method. Similar asymptotic
distribution of the empirical likelihood ratio statistic for stationary time
series has been derived to construct the confidence regions for the parameters.
However, required numerical problem of computing profile empirical likelihood
function which involves constrained maximization has no solution sometimes,
which leads to the drawbacks of using the original version of the empirical
likelihood ratio. In this paper, we propose an adjusted empirical likelihood
ratio statistic to modify the one proposed by Monti so that it guarantees the
existence of the solution of the required maximization problem, while
maintaining the similar asymptotic properties as Monti obtained. Simulations
have been conducted to illustrate the coverage probabilities obtained by the
adjusted version for different time series models which are better than the
ones obtained by Monti's version, especially for small sample sizes.
