How to deal with missing observations in surveys of professional forecasters

Survey forecasts are prone to entry and exit of forecasters as well as forecasters not contributing every period leading to gaps. These gaps make it difficult to compare individual forecasters to each other and raises the question of how to deal with the missing observations. This is addressed for the variables GDP, CPI inflation, and unemployment for the US. The theoretically optimal method of filling in missing observations is derived and compared to several competing methods. It is found that not filling in missing observations and taking the previous value do not perform particularly well. For the other methods assessed, there is no clear superior approach for all use cases, but the theoretically optimal one usually performs quite well.