Topics in Advanced Econometrics : Volume II Linear and Nonlinear Simultaneous Equations

by Phoebus J. Dhrymes

This textbook is intended for graduate students and professionals who have an interest in linear and nonlinear simultaneous equation models. These models arise in a great many settings in econometrics. The author's aim is to present a readable account, starting from an introduction to the general linear structural econometric model. From there, the book covers the identification problem, maximum likelihood methods, two and three stage least square methods, the general nonlinear model, and more advanced topics such as the general nonlinear simultaneous equations model. The reader is assumed to have a basic background in probability theory but otherwise this account is self-contained

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Year of publication:	1994
Authors:	Dhrymes, Phoebus J.
Publisher:	New York, NY : Springer New York
Subject:	Ökonometrie \| Econometrics \| Mehrgleichungsmodell \| Multiple equation model \| Statistische Methodenlehre \| Statistical theory \| Schätztheorie \| Estimation theory

Extent:	Online-Ressource (XVII, 401p) online resource
Type of publication:	Book / Working Paper
Language:	English
Notes:	1 Extension of Classical Methods I1.1 Introduction -- 1.2 A Brief Historical Review -- 1.3 The Nature of the GLSEM -- 1.4 The GLSEM: Assumptions and Notation -- 1.5 Inconsistency of OLS Estimators -- 1.6 Two Stage Least Squares (2SLS) -- 1.7 Three Stage Least Squares (3SLS) -- 1.8 Restricted 2SLS and 3SLS Estimators -- 1.9 Tests of Prior Restrictions -- Appendix to Chapter 1 -- 2 Extension of Classical Methods II -- 2.1 Limiting Distributions -- 2.2 Forecasting from the GLSEM -- 2.3 The Vector Autoregressive Model (VAR) -- 2.4 Instrumental Variables (IV) -- 2.5 IV and Insufficient Sample Size -- 2.6 k-class and Double k-class Estimators -- 2.7 Distribution of LM Derived Estimators -- 2.8 Properties of Specification Tests -- Appendix to Chapter 2 -- 3 Maximum Likelihood Methods I -- 3.1 Introduction -- 3.2 The Identification Problem -- 3.3 ML Estimation of the RF -- 3.4 FIML Estimation -- 3.5 Simplified FIML Estimators -- 3.6 Properties of Simplified Estimators -- 3.7 Limiting Distribution of FIML -- 4 LIML Estimation Methods -- 4.1 The “Concentrated” Likelihood Function -- 4.2 The Single Equation LIML Estimator -- 4.3 Consistency of the LIML Estimator -- 4.4 An Interesting Interpretation of LIML -- 4.5 Indirect Least Squares (ILS) -- 4.6 Relation of LIML to Other Estimators -- 4.7 Limiting Distribution of LIML Estimators -- 4.8 Classic Identifiability Tests -- Appendix to Chapter 4 -- 5 Nonlinear ML Methods -- 5.1 Motivation -- 5.2 A Mathematical Digression -- 5.3 Aspects of Likelihood Functions -- 5.4 Fisher Information -- 5.5 The Cramer-Rao Bounds -- 5.6 Martingale Properties of Likelihood Functions -- 5.7 Kullback Information -- 5.8 Convergence A.C. of ML Estimators -- 5.9 The General Nonlinear Model (GNLM) -- 5.10 The GNLM with Restrictions -- 5.11 Tests of Restrictions -- 6 Topics in NLSE Theory -- 6.1 Nonlinear ML -- 6.2 Nonlinear 2SLS -- 6.3 Nonlinear 3SLS -- 6.4 GMM -- 6.5 Causality and Related Issues.
ISBN:	978-1-4612-4302-1 ; 978-1-4612-8731-5
Other identifiers:	10.1007/978-1-4612-4302-1 [DOI]
Source:	ECONIS - Online Catalogue of the ZBW

Persistent link: https://www.econbiz.de/10013522047