Functional coefficient autoregressive models: Estimation and tests of hypotheses - Rong Chen (Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes, 1998)
In this paper we study nonparametric estimation and hypothesis testing procedures for the functional coefficient AR (FAR) models of the form Xt = f1(Xt-d)Xt-1 +…+ fp(Xt-d)Xt-p +εt, first proposed by Chen and Tsay (1993). As a direct generalization of the linear AR model, the FAR model is a rich class of models that includes many successful parametric nonlinear time series models such as the threshold AR models of Tong (1983), exponential AR models of Haggan and Ozaki (1978) and many others. We propose a local linear estimation procedure for estimating the coefficient functions and study its asymptotic properties. In addition, we propose two testing procedures. The first one tests whether all the coefficient functions are constant (i.e. whether the process is linear). The second one tests if all the coefficient functions are continuous, (i.e. if any threshold type of nonlinearity presents in the process). Some simulation results are presented.
urn:nbn:de:kobv:11-10056443
Keywords: 330; Continuity test; Linearity test; Local linear estimation; Nonparametric estimation; One sided kernel; Threshold Model