A note on multi-step forecasting with functional coefficient autoregressive models |
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Authors: | Jane L Bonnie K |
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Institution: | aDepartment of Mathematics and Statistics, Mississippi State University, Mississippi State, MS 39762-9715, United States;bDepartment of Mathematical Sciences, IBM T.J. Watson Research Center, Yorktown Heights, NY 10598, United States |
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Abstract: | This paper presents and evaluates alternative methods for multi-step forecasting using univariate and multivariate functional coefficient autoregressive (FCAR) models. The methods include a simple “plug-in” approach, a bootstrap-based approach, and a multi-stage smoothing approach, where the functional coefficients are updated at each step to incorporate information from the time series captured in the previous predictions. The three methods are applied to a series of U.S. GNP and unemployment data to compare performance in practice. We find that the bootstrap-based approach out-performs the other two methods for nonlinear prediction, and that little forecast accuracy is sacrificed using any of the methods if the underlying process is actually linear. |
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Keywords: | Bootstrap prediction Multi-step prediction Smoothing Vector nonlinear time series |
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