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Activity Number: 344
Type: Contributed
Date/Time: Tuesday, July 31, 2012 : 10:30 AM to 12:20 PM
Sponsor: Business and Economic Statistics Section
Abstract - #306503
Title: Functional Coefficient Autoregressive Models for Nonlinear Time Series
Author(s): Alireza Tahai*+
Companies: Mississippi State University
Address: Box9582, Mississippi State, MS, 39762,
Keywords: Linearity test; Local linear estimation;

Constructing models from time series with nontrivial dynamics involves the problem of how to choose the best model from within a class of models, or to choose between competing classes. The Functional Coefficient Autoregressive (FAR) model is a rich class of models that includes many successful parametric nonlinear time series models such as the threshold Autoregressive models of Tong (1983), exponential AR models of Haggan and Ozaki (1978) and many others. The Taylor expansion of the exponential AR model around a given point in the sample space is used to examine the dynamic relationship of the model. The built model is close to the simplest possible according to a description length criterion. The local linear regression is applied to estimate FAR model for time series data. Citations Granger, C. W. J., and T. Ter¨asvirta (1993). Modelling Nonlinear Economic Relationships. New York: Oxford University Press. Haggan v., T. Ozaki (1981). "Modeling nonlinear vibrations using an amplitude dependent autoregressive time series model." Biometrica, Vol. 68,189-196. Tong, H. (1983). Threshold Models in Non-linear Time Series Analysis: Lecture Notes in Statistics 21. Berlin: Springer-Ve

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