Abstract Details
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Activity Number:
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415
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Type:
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Contributed
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Date/Time:
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Tuesday, August 5, 2014 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Nonparametric Statistics
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Abstract #311730
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Title:
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Spline-Backfitted Kernel Forecasting for Functional-Coefficient Autoregressive Models
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Author(s):
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Joshua Patrick*+
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Companies:
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University of California, Davis
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Keywords:
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forecasting ;
nonlinear time series ;
spline-backfitted kernel estimation
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Abstract:
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The functional coefficient autoregressive (FCAR) model is a useful structure for reducing the size of the class of nonlinear time series models. This reduction in class is done by imposing coefficient functions in the autoregressive model. Spline-backfitted kernel smoothing (SBK) has been shown to be a computationally expedient and reliable method for estimating these models. We propose three forecasting methods that uses a SBK approach for estimating the coefficient functions. The performance of the SBK forecasts is analyzed through simulations and a solar irradiance data example.
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Authors who are presenting talks have a * after their name.
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