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Parameter estimation in a class of heteroscedastic time series models isinvestigated. The existence of conditional least-squares and conditionallikelihood estimators is proved. Their consistency and their asymptoticnormality are established. Kernel estimators of the noise's density and itsderivatives are defined and shown to be uniformly consistent. A simulationexperiment conducted shows that the estimators perform well for large samplesize.

Estimation in a class of nonlinear heteroscedastic time series models