Dynamic Nonlinear Econometric Models

The book leads the reader to the frontier of research on asymptotic inference in dynamic nonlinear models.

Klappentext

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.

Inhalt
1 Introduction.- 2 Models, Data Generating Processes, and Estimators.- 3 Basic Structure of the Classical Consistency Proof.- 4 Further Comments on Consistency Proofs.- 5 Uniform Laws of Large Numbers.- 6 Approximation Concepts and Limit Theorems.- 7 Consistency: Catalogues of Assumptions.- 8 Basic Structure of the Asymptotic Normality Proof.- 9 Asymptotic Normality under Nonstandard Conditions.- 10 Central Limit Theorems.- 11 Asymptotic Normality: Catalogues of Assumptions.- 12 Heteroskedasticity and Autocorrelation Robust Estimation of Variance Covariance Matrices.- 13 Consistent Variance Covariance Matrix Estimation: Catalogues of Assumptions.- 14 Quasi Maximum Likelihood Estimation of Dynamic Nonlinear Simultaneous Systems.- 15 Concluding Remarks.- A Proofs for Chapter 3.- B Proofs for Chapter 4.- C Proofs for Chapter 5.- D Proofs for Chapter 6.- E Proofs for Chapter 7.- F Proofs for Chapter 8.- G Proofs for Chapter 10.- H Proofs for Chapter 11.- I Proofs for Chapter 12.- J Proofs for Chapter 13.- K Proofs for Chapter 14.- References.