In this article we propose a quasi-Whittle estimator for parametric families of time series models in the presence of missing data. This estimator extends results to the incompletely observed case. This extension is valid to non-Gaussian and non-linear models. It also allows to bound the variance of an associated quasi-periodogramm. A simulation study validates empirically the proposed estimate for mixing and non-mixing models.
