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Activity Number: 262
Type: Invited
Date/Time: Tuesday, July 31, 2007 : 10:30 AM to 12:20 PM
Sponsor: Business and Economics Statistics Section
Abstract - #308032
Title: New Models for Seasonal Time Series and Seasonal Adjustment
Author(s): David Findley*+ and Brian Monsell and John Aston and Jyh-Ying Peng
Companies: U.S. Census Bureau and U.S. Census Bureau and Academia Sinica and Academia Sinica
Address: DIR, Room 5K152B, Washington, DC, 20233,
Keywords: RegARIMA models ; Trading day effects ; X-12-ARIMA ; X-13A-S
Abstract:

Models with regression mean functions and seasonal ARIMA disturbances are heavily used by the seasonal adjustment community. We will describe, and show empirical results for, a new regression model for trading day effects in inventory (stock) series and a new class of seasonal ARIMA models. The new regression model is a constrained form of the stock trading day model of Bell (1984) obtained by imposing constraints on the flow series trading day coefficients, a procedure for which we have developed general methods. The new ARIMA models decompose the seasonal moving average factor of Box-Jenkins seasonal models into a product of two or three factors, each with its own coefficient, which is associated with a subset of the frequencies, 0, 1, ., 6 cycles per year.

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Revised September, 2007