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Abstract Details
Activity Number:
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450
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 1, 2012 : 8:30 AM to 10:20 AM
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Sponsor:
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Business and Economic Statistics Section
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Abstract - #306332 |
Title:
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Coupling X-12-Arima-Seats with a Multidimensional Direct Filter Approach for Signal Extraction in Nonstationary Seasonal Time Series
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Author(s):
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Chris Blakely*+
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Companies:
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U.S. Department of Commerce
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Address:
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2647 Connecticut Ave. NW, Washington, DC, 20008, United States
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Keywords:
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Signal Extraction ;
X-12-ARIMA-SEATS ;
Direct Filter Approach ;
Seasonal Adjustment
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Abstract:
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This paper introduces a coupling strategy of model-based signal extraction with a newly developed iterative approach to multidimensional direct concurrent filtering (M-DFA). The process of extracting desired signals in nonstationary seasonal time series is fortified using a three-step approach to obtain better control over user-defined properties. These properties can include timeliness of turning-points in trend and/or cycles, signal-noise ratio characteristics, and quality of seasonal adjustment. Futhermore, we show how the uSim software developed by the author simplifies this hybrid approach of user-defined signal extraction using a simple combination of graphical-user-interface controls for adjusting regARIMA model fitting, X-12-ARIMA-SEATS signal decompostions, and timeliness/accuracy controls in an iterative M-DFA algorithm. Finally, we give numerous numerical examples to demonstrate the potential and robustness of the proposed hybrid method.
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Authors who are presenting talks have a * after their name.
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