  Parameter estimation in a class of heteroscedastic time series models is
investigated. The existence of conditional least-squares and conditional
likelihood estimators is proved. Their consistency and their asymptotic
normality are established. Kernel estimators of the noise's density and its
derivatives are defined and shown to be uniformly consistent. A simulation
experiment conducted shows that the estimators perform well for large sample
size.
