This paper proposes an extended model based on ACR nmdel： Functional coefficient autoregressive conditional root model （FCACR）. Under some assumptions, the authors show that the process is geometrically ergodic, st...This paper proposes an extended model based on ACR nmdel： Functional coefficient autoregressive conditional root model （FCACR）. Under some assumptions, the authors show that the process is geometrically ergodic, stationary and all moments of the process exist. The authors use the polynomial spline function to approximate the functional coefficient, and show that the estimate is consistent with the rate of convergence Op（hv＋1 ＋ n-1/3）. By simulation study, the authors discover the proposed method can approximate well the real model. Furthermore, the authors apply the model to real exchange rate data analysis.展开更多
基金supported by the National Nature Science Foundation of China under Grant Nos.10961026, 11171293,71003100,70221001,70331001,and 10628104the Ph.D.Special Scientific Research Foundation of Chinese University under Grant No.20115301110004+2 种基金Key Fund of Yunnan Province under Grant No.2010CC003the Fundamental Research Funds for the Central Universitiesthe Research Funds of Renmin University of China under Grant No.11XNK027
文摘This paper proposes an extended model based on ACR nmdel： Functional coefficient autoregressive conditional root model （FCACR）. Under some assumptions, the authors show that the process is geometrically ergodic, stationary and all moments of the process exist. The authors use the polynomial spline function to approximate the functional coefficient, and show that the estimate is consistent with the rate of convergence Op（hv＋1 ＋ n-1/3）. By simulation study, the authors discover the proposed method can approximate well the real model. Furthermore, the authors apply the model to real exchange rate data analysis.
