JSM 2005 - Toronto

Abstract #303440

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Activity Number: 399
Type: Contributed
Date/Time: Wednesday, August 10, 2005 : 10:30 AM to 12:20 PM
Sponsor: Business and Economics Statistics Section
Abstract - #303440
Title: Statistical Properties of Signal Extraction Diagnostics
Author(s): Tucker McElroy*+
Companies: U.S. Census Bureau
Address: 4700 Silver Hill Road, Washington, DC, 20233, United States
Keywords: Seasonal Adjustment ; Signal Extraction ; Nonstationary Time Series ; Diagnostic ; Spectral Density
Abstract:

A model-based diagnostic for signal extraction was first described in Maravall (2003). This basic idea was modified and studied in Findley, McElroy, and Wills (2004). This paper 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 described explicitly. A further modified diagnostic provides an interpretation of one-sided rejection of the Null Hypothesis, yielding general notions of ``over-smoothing" and ``under-smoothing." Asymptotic power calculations help explain why under-smoothing is harder to detect. The new methods are demonstrated on a U.S. Census Bureau time series exhibiting seasonality.

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