U.S. Department of Commerce

X-13ARIMA-SEATS Seasonal Adjustment Program

You are here: Census.govSubjects A to ZX-13ARIMA-SEATSSeasonal Adjustment PapersPapers by Year › Abstract of McElroy (2005b)
Skip top of page navigation

A Spectral Approach for Locally Assessing Time Series Model Misspecification

Tucker S. McElroy(1)

ABSTRACT:

A model-based diagnostic for signal extraction was first described in Maravall (2003), and this basic idea was modified and studied in Findley, McElroy, and Wills (2004). The paper at hand improves on the latter work in two ways: central limit theorems for the diagnostics are developed, and two hypothesis-testing paradigms for practical use are explicitly described. A further modified diagnostic provides an interpretation of one-sided rejection of the Null Hypothesis, yielding general notions of "over-smoothing" and "under-smoothing." The new methods are demonstrated on a U.S. Census Bureau time series exhibiting seasonality.

KEYWORDS:

ARIMA model, Seasonal adjustment, Filtering, Central limit theorem.





(1) Tucker S. McElroy is Mathematical Statistican, Center for Statistical Research and Methodology U. S. Census Bureau, 4600 Silver Hill Road, Washington, DC 20233. email : Tucker.S.McElroy@census.gov


[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 | Center for Statistical Research and Methodology | (301) 763-1649 (or x12@census.gov) |  Last Revised: November 19, 2012