QUASI-MAXIMUM LIKELIHOOD ESTIMATION FOR A CLASS OF CONTINUOUS-TIME LONG-MEMORY PROCESSES By Henghsiu Tsai and K. S. Chan Academia Sinica and University of Iowa
نویسندگان
چکیده
Tsai and Chan (2003) has recently introduced the Continuous-time AutoRegressive Fractionally Integrated Moving-Average (CARFIMA) models useful for studying long-memory data. We consider the estimation of the CARFIMA models with discrete-time data by maximizing the Whittle likelihood. We show that the quasimaximum likelihood estimator is asymptotically normal and efficient. Finite-sample properties of the quasi-maximum likelihood estimator and those of the exact maximum likelihood estimator are compared by simulations. Simulations suggest that for finite samples, the quasi-maximum likelihood estimator of the Hurst parameter is less biased but more variable than the exact maximum likelihood estimator. We illustrate the method with a real application.
منابع مشابه
Maximum likelihood estimation of linear continuous- time long-memory processes with discrete-time data
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