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In the first part of this article, we briefly review the history of seasonal adjustment and statistical time series analysis in order to understand why seasonal adjustment methods have evolved into their present form. This review provides insight into some of the problems that must be addressed by seasonal adjustment procedures and points out that advances in modern time series analysis raise the question of whether seasonal adjustment should be performed at all. This in turn leads to a discussion in the second part of issues involved in seasonal adjustment. We state our opinions about the issues raised and review some of the work of other authors. First, we comment on reasons that have been given for doing seasonal adjustment and suggest a new possible justification. We then emphasize the need to define precisely the seasonal and nonseasonal components and offer our definitions. Finally, we discuss criteria for evaluating seasonal adjustments. We contend that proposed criteria based on empirical comparisons of estimated components are of little value and suggest that seasonal adjustment methods should be evaluated based on whether they are consistent with the information in the observed data. This idea is illustrated with an example. [ABSTRACT FROM AUTHOR]

Population estimates from the 1990 Post-Enumeration Survey (PES), used to measure decennial census undercount, were obtained from dual system estimates (DSE's) that assumed independence within strata defined by age-race-sex-geography and other variables. We make this independence assumption for females, but develop methods to avoid the Independence assumption for males within strata by using national level sex ratios from demographic analysis (DA). This is done by using DSE results for females and the DA sex ratios to determine national level control totals for male population by age-race groups. These control totals are then used to determine some function of the individual strata 2 x 2 table probabilities for males that is assumed constant across strata within age-race groups. One such candidate function is the cross-product ratio, but other functions can be used that lead to different DSEs. We consider several such alternative DSE's, and use DA results for 1990 to apply them to data from the 1990 U S census and PES. [ABSTRACT FROM AUTHOR]

Focuses on the implications of unit roots on the time series models. Details on the stochastic difference equation model; Description of the unit roots; Demonstrations of test for autoregressive models.

Responds to the comments and suggestions of several scientists regarding the X-12 ARIMA program introduced by the United States Census Bureau. Changes in the seasonal pattern of the program; Effects of changing the program; Revisions made in the program; Other suggestions for the improvement of the computer program.

Presents information on the United States Census Bureau's X-12-ARIMA seasonal-adjustment computer program. Four types of enhancements in the program; Details on how the program was introduced; Regarima modeling and model selection.

In the first part of this article, we briefly review the history of seasonal adjustment and statistical time series analysis in order to understand why seasonal adjustment methods have evolved into their present form. This review provides insight into some of the problems that must be addressed by seasonal adjustment procedures and points out that advances in modem time series analysis raise the question of whether seasonal adjustment should be performed at all This in turn leads to a discussion in the second part of issues involved in seasonal adjustment. We state our opinions about the issues raised and review some of the work of other authors. First, we comment on reasons that have been given for doing seasonal adjustment and suggest a new possible justification. We then emphasize the need to define precisely the seasonal and nonseasonal components and offer our definitions. Finally, we discuss criteria for evaluating seasonal adjustments. We contend that proposed criteria based on empirical comparisons of estimated components are of little value and suggest that seasonal adjustment methods should be evaluated based on whether they are consistent with the information in the observed data. This idea is illustrated with an example. [ABSTRACT FROM AUTHOR]

Presents the authors' reply to comments on their article on seasonal adjustment of economic time-series, that appear in the October 1984 issue of the "Journal of Business and Economic Statistics," vol. 2. Reasons for seasonal adjustment; Criteria for evaluating seasonal adjustments; Information on the use of autoregressive integrated moving average models to calculate seasonal adjustment.

Responds to a commentary to an article concerning the analysis of seasonal adjustment of economic time series. Problems associated with modeling seasonal time series; Comments on the applicability of ARIMA model-based seasonal adjustment; Reconstruction of unadjusted economic data.

The article comments on the study conducted by Ghysels, Granger, and Siklos (GGS) to examine potential nonlinearities in the X-11 seasonal-adjustment program. GGS investigates the topic using different techniques from those of Young (1968) and report a different conclusion. GGS concludes that X-11 is far from being a linear data-filtering process. GGS believes that the conclusion derives primarily from their use of improved analytical and computational techniques. The article suggests that GGS address a different question than did Young.

Reports on the contribution made by Danny Pfeffermann on the use of time series models in estimation for repeated surveys. Potential use of the approach in model-based seasonal adjustment; Key features of the time series model; Limitations of using only macro time series data for estimation of the population-mean and survey-error models; Difficulty in using both survey microdata and macro time series data in estimating the complete model.