Concurrent seasonal adjustment utilizes all information up to and including the current month's figure in forming seasonally adjusted data, and thus should provide more accurate estimates of final seasonally adjusted data than the prevalent official method where the seasonal component is forecasted from data through the preceding December. This paper evaluates the expected gain, in terms of the reduction in RMSE of seasonal revisions, from employing concurrent seasonal adjustment.

The framework of the paper is then extended to the case where the data contain no seasonal as well as seasonal revisions, the former resulting from "preliminary-data” error in the first-published, not seasonally adjusted (NSA) data. It is found that the gain from concurrent adjustment is usually reduced, often substantially, by noise in preliminary NSA data. However, an offset to this effect also occurs since the forecasted seasonal component must also be derived from preliminary data.

Some of the paper's results are applied to a linearized X-ll-ARIMA procedure, using a common seasonal ARIMA model. An analysis of actual series containing preliminary-data error provides confirmation of the main results.