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.
Produktinformation
Utgivningsdatum1997-07-17
Mått155 x 235 x 22 mm
Vikt658 g
FormatInbunden
SpråkEngelska
Antal sidor312
FörlagSpringer-Verlag Berlin and Heidelberg GmbH & Co. KG
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.