Topics In Advanced Econometrics: Volume Ii Linear And Nonlinear Simultaneou... - encadernada, livro de bolso

EAN (ISBN-13): 9780387941561
ISBN (ISBN-10): 0387941568
Livro de capa dura
Ano de publicação: 2007
Editor/Editora: Springer New York
424 Páginas
Peso: 0,795 kg
Língua: eng/Englisch

Livro na base de dados desde 2007-11-06T14:37:19-02:00 (Sao Paulo)
Página de detalhes modificada pela última vez em 2023-08-02T15:07:56-03:00 (Sao Paulo)
Número ISBN/EAN: 9780387941561

Número ISBN - Ortografia alternativa:
0-387-94156-8, 978-0-387-94156-1
Ortografia alternativa e termos de pesquisa relacionados:
Autor do livro: dhrymes, phöbus
Título do livro: topics advanced econometrics, linear and nonlinear

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Dados da editora

Autor: Phoebus J. Dhrymes
Título: Topics In Advanced Econometrics - Volume II Linear and Nonlinear Simultaneous Equations
Editora: Springer; Springer US
402 Páginas
Ano de publicação: 1994-01-07
New York; NY; US
Peso: 1,700 kg
Língua: Inglês
53,49 € (DE)
54,99 € (AT)
59,00 CHF (CH)
POD
XVIII, 402 p.

BB; Economic Theory/Quantitative Economics/Mathematical Methods; Hardcover, Softcover / Wirtschaft/Volkswirtschaft; Wirtschaftstheorie und -philosophie; Verstehen; econometrics; forecasting; inequality; probability theory; value-at-risk; Quantitative Economics; BC; EA

1 Extension of Classical Methods I.- 1.1 Introduction.- 1.2 A Brief Historical Review.- 1.3 The Nature of the GLSEM.- 1.4 The GLSEM: Assumptions and Notation.- 1.4.1 Assumptions and Conventions.- 1.4.2 Notation.- 1.5 Inconsistency of OLS Estimators.- 1.6 Two Stage Least Squares (2SLS).- 1.6.1 The Original Derivation.- 1.6.2 An Alternative Formulation.- 1.7 Three Stage Least Squares (3SLS).- 1.8 Restricted 2SLS and 3SLS Estimators.- 1.9 Tests of Prior Restrictions.- 1.9.1 Generalities.- 1.9.2 A Restricted Least Squares Interpretation of 2SLS and 3SLS.- Questions and Problems.- Appendix to Chapter 1.- Preliminaries to Hausman’s Test.- Examples.- 2 Extension of Classical Methods II.- 2.1 Limiting Distributions.- 2.1.1 Preliminaries.- 2.1.2 Limiting Distributions for Static GLSEM.- 2.1.3 Limiting Distributions for Dynamic GLSEM.- 2.2 Forecasting from the GLSEM.- 2.2.1 Generalities.- 2.2.2 Forecasting from the URF.- 2.2.3 Forecasting from the RRF.- 2.3 The Vector Autoregressive Model (VAR).- 2.4 Instrumental Variables (IV).- 2.4.1 2SLS and 3SLS as IV Estimators.- 2.4.2 2SLS and 3SLS as Optimal IV Estimators.- 2.5 IV and Insufficient Sample Size.- 2.5.1 The Nature of the Problem.- 2.5.2 Iterated Instrumental Variables (IIV).- 2.6 k-class and Double k-class Estimators.- 2.7 Distribution of LM Derived Estimators.- 2.8 Properties of Specification Tests.- 2.8.1 Single Equation 2SLS.- 2.8.2 Systemwide 2SLS and 3SLS.- 2.8.3 Relation to Hausman’s Test.- Questions and Problems.- Appendix to Chapter 2.- Convergence of Second Moment Matrices.- Convergence for Dependent Sequences.- Preliminaries and Miscellaneous.- Convergence of Second Moments of Final Form Errors.- 3 Maximum Likelihood Methods I.- 3.1 Introduction.- 3.2 The Identification Problem.- 3.2.1 Generalities.- 3.2.2 The Simple Supply-Demand Model.- 3.2.3 Identification by Exclusion Restrictions.- 3.2.4 Identification by Linear Restrictions.- 3.2.5 Identification and the Reduced Form.- 3.2.6 Covariance and Cross Equation Restrictions.- 3.2.7 A More General Framework.- 3.2.8 Parametric Nonlinearities and Identification.- 3.3 ML Estimation of the RF.- 3.3.1 General Discussion and ILS.- 3.3.2 Estimation of the Reduced Form.- 3.4 FIML Estimation.- 3.5 Simplified FIML Estimators.- 3.6 Properties of Simplified Estimators.- 3.6.1 Consistency.- 3.7 Limiting Distribution of FIML.- Questions and Problems.- 4 LIML Estimation Methods.- 4.1 The “Concentrated” Likelihood Function.- 4.1.1 A Subset of m* Structural Equations.- 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.- Questions and Problems.- Appendix to Chapter 4.- Limiting Distribution of (T? — 1).- 5 Nonlinear ML Methods.- 5.1 Motivation.- 5.2 A Mathematical Digression.- 5.3 Aspects of Likelihood Functions.- 5.3.1 An Interesting Inequality.- 5.4 Fisher Information.- 5.4.1 Alternative Representation of the Information Matrix.- 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.- 5.8.1 Independent Observations.- 5.8.2 Generalizations.- 5.9 The General Nonlinear Model (GNLM).- 5.9.1 Consistency.- 5.9.2 Identification.- 5.9.3 Asymptotic Normality.- 5.10 The GNLM with Restrictions.- 5.11 Tests of Restrictions.- 5.11.1 Generalities.- 5.11.2 The Conformity Test.- 5.11.3 The Likelihood Ratio Test.- 5.11.4 The Lagrange Multiplier Test.- 5.11.5 Equivalence of the Three Tests.- Questions and Problems.- 6 Topics in NLSE Theory.- 6.1 Nonlinear ML.- 6.1.1 Identification.- 6.1.2 Consistency of the ML Estimator.- 6.1.3 Limiting Distribution of ML Estimators.- 6.1.4 Relation of Structural and Covariance Parameter (ML) Estimators.- 6.1.5 Estimators in Structurally Misspecified Models.- 6.2 Nonlinear 2SLS.- 6.2.1 Identification and Consistency of NL2SLS.- 6.2.2 Asymptotic Normality of NL2SLS.- 6.2.3 Choice of an Optimal NL2SLS Estimator.- 6.3 Nonlinear 3SLS.- 6.3.1 Identification and Consistency of NL3SLS.- 6.3.2 Asymptotic Normality of NL3SLS.- 6.3.3 Optimum NL3SLS and Computational Aspects.- 6.4 GMM.- 6.4.1 Reformulation of GMM as NL2SLS and NL3SLS.- 6.4.2 Identification and Consistency.- 6.4.3 Asymptotic Normality.- 6.4.4 Tests of Restrictions.- 6.5 Causality and Related Issues.- 6.5.1 Introduction.- 6.5.2 Basic Concepts.- Questions and Problems.