Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/119170
Title: WHITTLE LIKELIHOOD ESTIMATION FOR NONLINEAR TIME SERIES
Authors: HUANG LEI
Keywords: ARMA, Autocorrelation, Semivarying, Spectrum,Likelihood, Dynamic
Issue Date: 13-Jan-2015
Citation: HUANG LEI (2015-01-13). WHITTLE LIKELIHOOD ESTIMATION FOR NONLINEAR TIME SERIES. ScholarBank@NUS Repository.
Abstract: SERIAL CORRELATION IN THE RESIDUALS OF TIME SERIES MODELS IS COMMONLY OBSERVED. HOWEVER, ESTIMATION OF THE RESULTANT MODELS THAT INCORPORATE THE CORRELATION IS VERY DIFFICULT, ESPECIALLY WHEN THE REGRESSION FUNCTION IS NONLINEAR. EXISTING ESTIMATION METHODS USUALLY REQUIRE STRONG ASSUMPTION BETWEEN THE RESIDUALS AND THE REGRESSORS, WHICH TYPICALLY EXCLUDES THE AUTOREGRESSIVE MODELS. BY EXTENDING THE WHITTLE LIKELIHOOD ESTIMATION, THIS DISSERTATION INVESTIGATES IN DETAILS A SEMI-PARAMETRIC AUTOREGRESSIVE MODEL WITH RESIDUALS OF ARMA SEQUENCE. ASYMPTOTIC NORMALITY OF THE ESTIMATORS IS ESTABLISHED, AND A MODEL SELECTION PROCEDURE IS PROPOSED. NUMERICAL EXAMPLES ARE EMPLOYED TO ILLUSTRATE THE PERFORMANCE OF THE PROPOSED ESTIMATION AND THE NECESSITY OF HANDLING THE SERIAL CORRELATED RESIDUALS. EXTENDED NONLINEAR DYNAMIC PANEL DATA MODEL WITH RESIDUALS OF ARMA SEQUENCE IS ALSO PROPOSED AND A MODIFIED ESTIMATION PROCEDURE IS ESTABLISHED, SEVERAL NUMERIC EXAMPLES ARE SIMULATED TO DEMONSTRATE T
URI: http://scholarbank.nus.edu.sg/handle/10635/119170
Appears in Collections:Ph.D Theses (Open)

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