On Concurrent Seasonal Adjustment
研究了同步季节性调整相比传统官方方法在估计最终季节性调整数据时的准确性提升，考虑了数据包含非季节性和季节性修正的情况，并应用于线性化X-11-ARIMA程序及实际序列分析。
Abstract Concurrent seasonal adjustment uses all information up to and including the current month's figure and thus should provide more accurate estimates of final seasonally adjusted data than the prevalent official method where the current month's component is forecasted from data through the preceding December. This article evaluates the expected gain from employing concurrent seasonal adjustment, including the case in which the data contain both nonseasonal and seasonal revisions. The theoretical results are then applied to a linearized X-11-ARIMA procedure, using a group of common seasonal autoregressive integrated moving average (ARIMA) models, and to an analysis of actual series containing preliminary-data error. Concurrent adjustment is consistently found to afford greater accuracy, though to a smaller degree when not seasonally adjusted (NSA) data are also preliminary. The problem of how best to seasonally adjust current data arises because both the usual (X-11) and the optimal (in a signal extr...