Work with interactive mapping tools from across the Census Bureau.
Read briefs and reports from Census Bureau experts.
Watch Census Bureau vignettes, testimonials, and video files.
Read research analyses from Census Bureau experts.
Developer portal to access services and documentation for the Census Bureau's APIs.
Explore Census Bureau data on your mobile device with interactive tools.
Find a multitude of DVDs, CDs and publications in print by topic.
These external sites provide more data.
Download extraction tools to help you get the in-depth data you need.
Explore Census data with interactive visualizations covering a broad range of topics.
Information about the U.S. Census Bureau.
Information about what we do at the U.S. Census Bureau.
Learn about other opportunities to collaborate with us.
Explore the rich historical background of an organization with roots almost as old as the nation.
Explore prospective positions available at the U.S. Census Bureau.
Information about the current field vacancies available at the U.S. Census Bureau Regional Offices.
Discover the latest in Census Bureau data releases, reports, and events.
The Census Bureau's Director writes on how we measure America's people, places and economy.
Find interesting and quirky statistics regarding national celebrations and major events.
Find media toolkits, advisories, and all the latest Census news.
See what's coming up in releases and reports.
The detection and estimation of business cycles in economic time series is an important activity of econometricians, and typically involves the filtering of one or more seasonally adjusted time series. The community of econometricians favoring univariate model-based approaches to cycle estimation seeks to avoid the identification of spurious cycles via taking a data-driven approach, which is in contrast to nonparametric band-pass approaches. However, given that seasonal adjustment is a procedure that greatly affects all frequencies of the raw data, it is natural to ask the following questions: can cycles be adequately detected from raw data? If so, are the detection rates superior to those obtained from seasonally adjusted data, and does this question depend on the method of adjustment? Does seasonal adjustment generate spurious cycles? This paper seeks to provide statistical methodology that can be used to answer these queries. We introduce a diagnostic statistic for deciding the inclusion or exclusion of an unobserved component, such as a cycle, and determine its theoretical properties. We then describe how this can be used to address our research questions in a rigorous fashion, and how currently available tools are not adequate.
ARIMA models, Goodness-of-fit, Signal extraction, Unobserved components
This symbol indicates a link to a non-government web site. Our linking to these sites does not constitute an endorsement of any products, services or the information found on them. Once you link to another site you are subject to the policies of the new site.