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The book provides an extensive discussion of asymptotic theory of M-estimators in the context of dynamic nonlinear models. The class of M-estimators contains least mean distance estimators (including maximum likelihood estimators) and generalized method of moments estimators. In addition to establishing the asymptotic properties of such estimators, the book provides a detailed discussion of the statistical and probabilistic tools necessary for such an analysis. The book also gives a careful treatment of estimators of asymptotic variance covariance matrices for dependent processes. TOC:From the contents: Preface.- Introduction.- Models, Data Generating Processes, and Estimators.- Basic Structure of the Classical Consistency Proof.- Further Comments on Consistency Proofs.- Uniform Laws of Large Numbers.- Approximation Concepts and Limit Theorems.- Consistency: Catalogues of Assumptions.- Basic Structure of the Asymptotic Normality Proof.- Asymptotic Normality under Nonstandard Conditions.- Central Limit Theorems.- Asymptotic Normality: Catalogues of Assumptions.- Heteroskedasticity and Autocorrelation Robust Estimation of Variance Covariance Matrices.- Consistent Variance Covariance Matrix Estimation: Catalogues of Assumptions.- Quast Maximum Likelihood Estimation of Dynamic Nonlinear Simultaneous Systems.- Concluding Remarks.- References.- Index.
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