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This is an over-view of some recent developments in asymptotic inference for dependent and not necessarily identically distributed observations. Diverse models of non-ergodic type (see §2 for definitions), and results on efficiency of estimators and tests will be discussed using a unified approach. Our aim in this chapter is to present the main ideas and general asymptotic results in an informal manner. More detailed treatment of specific problems discussed here is given in subsequent chapters.
KeywordsMaximum Likelihood Estimator Limit Distribution Asymptotic Variance Asymptotic Curvature Linear Stochastic Differential Equation
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