The development of methods to estimate and project demographic and health indicators is important to help monitor trends over time. In practice, estimation often occurs in situations where data are sparse or variability is high. Trends and projections may be unclear because of missing observations over time, or if the observed data do not follow a smooth trajectory. Determining how data observations should be modeled and smoothed over time is not always a straightforward process. The aim of this paper is to compare the characteristics and performance of different temporal smoothing techniques to gain a deeper understanding into which methods work well in different data availability situations and how sensitive the resulting estimates are to modeling decisions. This work was presented at IUSSP 2017.