Work with interactive mapping tools from across the Census Bureau.
Collection of audio features and sound bites.
The Census Bureau packages data and information into easy-to-understand visuals.
Browse Census Bureau images.
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.
How we provide the best mix of timeliness, relevancy, quality, and cost for the data we collect.
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 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.
Listen to audio files on fun facts, historical figures, and celebrations of the month.
Find media toolkits, advisories, and all the latest Census news.
See what's coming up in releases and reports.
This paper expands the estimation theory for both quasi-maximum likelihood estimates (QMLEs) and Least Squares estimates (LSEs) for potentially misspecified constrained VAR(p) models. Our main result is a linear formula for the QMLE of a constrained VAR(p), which generalizes the Yule-Walker formula for the unconstrained case. We make connections with the known LSE formula and the determinant of the forecast mean square error matrix, showing that the QMLEs for a constrained VAR(p) minimize this determinant; however, the QMLEs need not minimize the component entries of the mean square forecast error matrix, in contrast to the unconstrained case. An application to computing mean square forecast errors from misspecified models is discussed, and numerical comparisons of the different methods are presented and explored.
Tucker McElroy and David Findley. (2013). Fitting Constrained Vector Autoregression Models. Center for Statistical Research & Methodology Research Report Series (Statistics #2013-06). U.S. Census Bureau. Available online at <http://www.census.gov/srd/papers/pdf/rrs2013-06.pdf>.
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.