JSM 2005 - Toronto

Abstract #303350

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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 - #303350
Title: Model-based Analysis of Seasonal Heteroskedasticity in Census Bureau Construction Time Series
Author(s): Thomas M. Trimbur*+
Companies: U.S. Census Bureau
Address: 4700 Silver Hill Road, Washington, DC, 20233-9100, United States
Keywords: Kalman filter ; Kernels ; Seasonal Adjustment ; Seasonal Heteroscedasticity ; State space
Abstract:

Seasonal heteroskedasticity exists in a number of monthly time series from major statistical agencies such as the U.S. Census Bureau construction series. Accounting for such systematic variation in calendar month effects is crucial in estimating seasonal effects and movements in underlying trend and in assessing the uncertainty in such estimates. Previously, the commonly used approach to this phenomenon was to use X-11 seasonal filters of different lengths, but there are limitations to how well this procedure is able to adapt to seasonal heteroskedasticity for different datasets. The model-based approach introduced recently by Proietti (2003) provides a more flexible alternative to handling time varying seasonal influences. We investigate the performance of this method for a number of U.S. Census Bureau time series; holiday and trading-day effects are accounted for using the X-12 ARIMA program, and special emphasis is placed on the estimation of seasonal factors and associated degree of uncertainty. Examples include economic indicators such as regional housing starts and related variables on construction activity.

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