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Activity Number:
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513
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Type:
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Contributed
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Date/Time:
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Thursday, August 10, 2006 : 8:30 AM to 10:20 AM
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Sponsor:
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Business and Economics Statistics Section
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| Abstract - #306535 |
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Title:
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Nonparametric Transfer Function Model
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Author(s):
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Jun Liu*+ and Qiwei Yao and Rong Chen
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Companies:
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Georgia Southern University and London School of Economics and University of Illinois at Chicago
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Address:
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P.O. Box 8151, Statesboro, GA, 30460,
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Keywords:
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nonparametric regression ; local polynomial regression ; regression splines ; nonlinear time series analysis ; transfer function model ; ARIMA
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Abstract:
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A new approach is proposed to model the relationship between an output and some input time series, perturbed by correlated noise. The functional form of this relationship (the transfer function) is unknown but assumed to be smooth. We propose to model the transfer function by nonparametric smoothing methods and model the noise as an ARIMA process. By using nonparametric smoothing, the model is very flexible and can be used to model highly nonlinear relationships of unknown form; by modeling the noise, the correlation in the data is removed so the transfer function can be estimated more efficiently; additionally, the estimated ARIMA structure can be used to improve the forecasting performance. The estimation procedures are introduced and the asymptotic properties of the estimators are studied. The finite-sample properties of the estimators are studied by simulation and real-life examples.
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