Maximum likelihood estimation of linear continuous- time long-memory processes with discrete-time data
نویسندگان
چکیده
We develop a new class of Continuous-time Auto-Regressive Fractionally Integrated Moving-Average (CARFIMA) models which are useful for modelling regularly-spaced and irregularly-spaced discrete-time long-memory data. We derive the autocovariance function of a stationary CARFIMA model, and study maximum likelihood estimation of a regression model with CARFIMA errors, based on discrete-time data and via the innovations algorithm. It is shown that the maximum likelihood estimator is asymptotically normal, and its finite-sample properties are studied through simulation. The efficacy of the proposed approach is demonstrated with a dataset from an environmental study.
منابع مشابه
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
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 propert...
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