Abstract Details
Activity Number:
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119
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Type:
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Topic Contributed
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Date/Time:
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Monday, August 5, 2013 : 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 - #308587 |
Title:
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The Trilemma Between Accuracy, Timeliness, and Smoothness in Real-Time Forecasting and Signal Extraction
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Author(s):
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Marc Wildi*+ and tucker mcelroy
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Companies:
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and census bureau
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Keywords:
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Direct Filter Approach ;
trilemma ;
customization ;
accuracy ;
timeliness ;
snoothness
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Abstract:
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The classic model-based paradigm in time series analysis is rooted in the Wold decomposition of the data-generating process (DGP) into an uncorrelated ``white noise" process. By design, this universal decomposition is indifferent to particular features of a specific prediction problem (e.g., forecasting or signal extraction) -- features driven by the priorities of the data-users. A single optimization principle is proposed to address a plethora of prediction problems. In contrast, this paper proposes to reconcile problem structures, user priorities, and optimization principles into a general framework whose scope encompasses the classic approach. We introduce the generalized prediction problem (GPP), which serves to formalize optimization algorithms in either of two ways: by model fitting, via GPP mean-square minimization, or by the so-called direct filter approach (DFA). We provide theoretical results and practical algorithms for both approaches, and discuss the merits and limitations of each. Furthermore, we propose a novel decomposition of the mean square error (MSE) of each
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Authors who are presenting talks have a * after their name.
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