Single equation models are well established among academics and practitioners to perform temporal disaggregation of low frequency time series using available related series. In this paper, we propose an extension that exploits information from the cross-sectional dimension. More specifically, we suggest jointly estimating multiple Chow and Lin (1971) equations, one for each cross-sectional unit (e.g. country), restricting the coefficients to be the same across units in order to interpolate unitspecific data. Using actual data on real GDP and industrial production for euro area countries we provide evidence that this approach can result in more accurate interpolated time series for individual countries. The results suggest that the inclusion of time fixed effects, which is not feasible in standard single equation models, can be helpful in increasing accuracy of the resulting series.