Asymptotic Theory
Buch, Englisch, 312 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 1410 g
ISBN: 978-3-540-62857-6
Verlag: Springer Berlin Heidelberg
Many relationships in economics, and also in other fields, are both dynamic and nonlinear. A major advance in econometrics over the last fifteen years has been the development of a theory of estimation and inference for dy namic nonlinear models. This advance was accompanied by improvements in computer technology that facilitate the practical implementation of such estimation methods. In two articles in Econometric Reviews, i.e., Pötscher and Prucha {1991a,b), we provided -an expository discussion of the basic structure of the asymptotic theory of M-estimators in dynamic nonlinear models and a review of the literature up to the beginning of this decade. Among others, the class of M-estimators contains least mean distance estimators (includ ing maximum likelihood estimators) and generalized method of moment estimators. The present book expands and revises the discussion in those articles. It is geared towards the professional econometrician or statistician. Besides reviewing the literature we also presented in the above men tioned articles a number of then new results. One example is a consis tency result for the case where the identifiable uniqueness condition fails.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Wirtschaftswissenschaften Volkswirtschaftslehre Volkswirtschaftslehre Allgemein Wirtschaftstheorie, Wirtschaftsphilosophie
- Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Numerische Mathematik
- Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung
- Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
- Wirtschaftswissenschaften Volkswirtschaftslehre Volkswirtschaftslehre Allgemein Ökonometrie
- Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik
- Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle
Weitere Infos & Material
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.
