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Accession Number PB2014-101213
Title Multivariate Approach to Seasonal Adjustment.
Publication Date Aug 2013
Media Count 41p
Personal Author R. Greenaway-McGrevy
Abstract This paper suggests a new semi-parametric multivariate approach to seasonal ad- justment. The primary innovation is to use a large dimensional factor model of cross section dependence to estimate the trend component in the seasonal decomposition of each time series. Because the trend component is speci.ed to capture covariation be- tween the time series, common changes in the level of the time series are accommodated in the trend, and not in the seasonal component, of the decomposition. The seasonal components are thus less prone to distortion resulting from severe business cycle .uc- tuations than univariate .lter-based seasonal adjustment methods. We illustrate these points this using a dataset that spans the 2007-2009 recession in the US..
Keywords Businesses
Computer software
Economic indicators
Explanatory variables
Intervention variables
Occupational surveys
Seasonal factors
Statistical models

Source Agency Department of Commerce, Bureau of Economic Analysis
NTIS Subject Category 72F - Statistical Analysis
70F - Public Administration & Government
62 - Computers, Control & Information Theory
96 - Business & Economics
Corporate Author Bureau of Economic Analysis, Washington, DC.
Document Type Technical report
Title Note N/A
NTIS Issue Number 1402
Contract Number N/A

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