论文信息 - Asymptotic optimal inference for a class of nonlinear time series models

Asymptotic optimal inference for a class of nonlinear time series models

The local asymptotic normality (LAN) of the log-likelihood ratio for a class of Markovian nonlinear time series models is established using the approach of quadratic mean differentiability. The error process in the models considered is not necessarily Gaussian. As a consequence of the LAN property, asymptotically optimal estimators of the model parameters are derived. Also, asymptotically efficient tests for linearity are constructed. Several examples are discussed as special cases.

I. V. Basawa | I. Basawa | S. Hwang | S. Y. Hwang

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