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The Rewards and Challenges of Seasonally Adjusting a Short Series: Seasonal Adjustment Research for the U.S. Census Bureau's Quarterly Services Survey
The Rewards and Challenges of Seasonally Adjusting a Short Series: Seasonal Adjustment Research for the U.S. Census Bureau's Quarterly Services Survey
Abstract
The Quarterly Services Survey (QSS) was introduced in 2004 as the Federal Government’s first new economic indicator in nearly 30 years. The survey provides quarterly revenue estimates for information, technology, administrative, and health sectors of the service economy. As the time series has lengthened for these industries, the Census Bureau has researched the feasibility of seasonally adjusting the data. This paper describes the research conducted to determine appropriate methods for producing seasonally adjusted estimates for QSS. Using X-12-ARIMA software diagnostics, we first identify those series that have a stable seasonal component. Then, we determine the industry levels at which the adjustment should be performed, the type of adjustment to be used, and the appropriate ARIMA model. This paper also summarizes the challenges that short data series pose to seasonal adjustment and to the X-12-ARIMA software.
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Seasonal Adjustment to Facilitate Forecasting: Empirical Results
In this paper we consider how well seasonal adjustment methods, and certain of their variations, satisfy one objective of seasonal adjustment.
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Page Last Revised - October 8, 2021
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