Skip Main Navigation Skip To Navigation Content

Research Reports

You are here: Census.govSubjects A to ZResearch Reports Sorted by Year › Abstract of RRS2007/14
Skip top of page navigation

NEW ARIMA MODELS FOR SEASONAL TIME SERIES AND THEIR APPLICATION TO SEASONAL ADJUSTMENT AND FORECASTING

John A.D. Aston, David F. Findley,Tucker S. McElroy, Kellie C. Wills, and Donald E.K. Martin

KEY WORDS: Airline model; Frequency-Specific Model; Generalized Airline Model; Model selection; AIC; F-AIC

ABSTRACT

Focusing on the widely-used Box-Jenkins “airline” model, we show how the class of seasonal ARIMA models with a seasonal moving average factor can be parsimoniously generalized to model time series with heteroskedastic seasonal frequency components. Our frequency-specific models decompose this factor by associating one moving average coefficient with a proper subset of the seasonal frequencies 1, 2, 3, 4, 5 and 6 cycles per year and a second coefficient with the complementary subset. A generalization of Akaike’s AIC is presented to determine these subsets. Properties of seasonal adjustment filters and adjustments obtained from the new models are examined as are forecasts.

CITATION:

Source: U.S. Census Bureau, Statistical Research Division

Created: October 18, 2007
Last revised: October 18, 2007


[PDF] or PDF denotes a file in Adobe’s Portable Document Format. To view the file, you will need the Adobe® Reader® Off Site available free from Adobe.

This symbol Off Site 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.

Source: U.S. Census Bureau | Statistical Research Division | (301) 763-3215 (or chad.eric.russell@census.gov) |   Last Revised: October 08, 2010