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Activity Number: 2
Type: Invited
Date/Time: Sunday, August 6, 2006 : 2:00 PM to 3:50 PM
Sponsor: Business and Economics Statistics Section
Abstract - #305194
Title: A Spectral Approach for Locally Assessing Model Misspecification
Author(s): Tucker S. McElroy and Scott Holan*+
Companies: U.S. Census Bureau and University of Missouri-Columbia
Address: Department of Statistics, Columbia, MO, 65211-6100,
Keywords: acceleration ; kernel ; seasonal adjustment ; spectral density
Abstract:

Peaks in the spectrum of a stationary process are indicative of periodic phenomenon, such as seasonality or business cycles. To this end, an important aspect of developing parametric models for periodic processes is proper characterization of spectral peaks. By using an aggregate measure of acceleration, this work proposes to test whether a hypothesized model is supported locally by the data. This technique is useful for detecting model misestimation. More specifically, it can be used to determine the appropriateness of a hypothesized seasonal adjustment model. Finally, the diagnostic is investigated through simulation and application to several seasonal series.

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Revised April, 2006