Asymptotic theory of least squares estimator of a nonlinear time series regression model
Debasis Kundu, Amit Mitra · Communication in Statistics- Theory and Methods · 1996
The consistency and asymptotic normality of the least squares estimator are derived of a particular non-linear time series model. It does not satisfy the standard sufficient conditions of Jennrich (1969) or Wu (1981). The errors are assumed to be independently and identically distributed random vaiiables each with mean zero and finite variance. Walker (1971) considered the same model and obtained the asymptotic properties of an approximate least squares estimator. It is observed that the least squares estimator and the approximate least squares estimator are asymptotically equal. Some simulations have hrcn performed to compare the two for small samples.