This paper reviews research issues in modeling panels of time series. Examples of this type of data are annually observed macroeconomic indicators for all countries in the world, daily returns on the individual stocks listed in the S&P500, and the sales records of all items in a retail store. A panel of time series usually concerns the case where the cross-section dimension and the time dimension are large. Usually, there is no a priori reason to select a few series or to aggregate the series over the cross-section dimension. In that case, however, the use of for example a vector autoregression or other types of multivariate systems becomes cumbersome. Panel models and associated estimation techniques are more useful. This paper discusses representation, estimation and inference in case the data have trends, seasonality, outliers, or nonlinearity. Various examples illustrate the various models.