An information theoretic approach to econometrics / George G. Judge, Ron C. Mittelhammer.

Format:

Book

Author/Creator:

Judge, George G., author.

Mittelhammer, Ron, author.

Language:

English

Subjects (All):

Econometrics.

Physical Description:

1 online resource (xvi, 232 pages) : digital, PDF file(s).

Place of Publication:

Cambridge : Cambridge University Press, 2012.

Language Note:

English

Summary:

This book is intended to provide the reader with a firm conceptual and empirical understanding of basic information-theoretic econometric models and methods. Because most data are observational, practitioners work with indirect noisy observations and ill-posed econometric models in the form of stochastic inverse problems. Consequently, traditional econometric methods in many cases are not applicable for answering many of the quantitative questions that analysts wish to ask. After initial chapters deal with parametric and semiparametric linear probability models, the focus turns to solving nonparametric stochastic inverse problems. In succeeding chapters, a family of power divergence measure-likelihood functions are introduced for a range of traditional and nontraditional econometric-model problems. Finally, within either an empirical maximum likelihood or loss context, Ron C. Mittelhammer and George G. Judge suggest a basis for choosing a member of the divergence family.

Contents:

Cover; AN INFORMATION THEORETIC APPROACH TO ECONOMETRICS; Title; Copyright; Dedication; Contents; Preface; ONE Econometric Information Recovery; 1.1 Book Objectives and Problem Format; 1.2 Organization of the Book; 1.3 Selected References; PART I TRADITIONAL PARAMETRIC AND SEMIPARAMETRIC ECONOMETRIC MODELS: ESTIMATION AND INFERENCE; TWO Formulation and Analysis of Parametric and Semiparametric Linear Models; 2.1 Data Sampling Processes (DSPs)and Notation; 2.2 A Parametric General Linear Model; 2.2.1 The Parametric Model and Maximum Likelihood (ML) Estimation of β and σ2

2.2.2 The Parametric Model and Inference2.3 A Semiparametric General Linear Model; 2.3.1 The Squared Error Metric and the Least Squares (LS) Principle; 2.3.2 The LS Estimator; 2.3.3 Finite Sample Statistical Properties of the LS Estimator; 2.3.4 Consistency and Asymptotic Normality of the LS Estimator; 2.3.5 Linear Semiparametric Model Inference; 2.3.6 Inferential Asymptotics; 2.3.7 Hypothesis Testing: Linear Equality Restrictions on β; 2.4 General Linear Model with Stochastic X; 2.4.1 Linear Model Assumptions; 2.4.2 LS Estimator Properties: Finite Samples

2.4.3 LS Estimator Properties: Asymptotics2.4.4 ML Estimation of and under Conditional Normality; 2.4.5 Hypothesis Testing and Confidence Region Estimation; 2.4.5a Semiparametric Case; 2.4.5b Parametric Case; 2.4.6 Summary: Statistical Implications of Stochastic X; 2.5 Extremum (E) Estimation and Inference; 2.5.1 ML and LS Estimators Expressed in E Estimator Form; 2.5.2 Asymptotic Properties of E Estimators; 2.5.3 Inference Based on E Estimation; 2.5.4 Summary and Forward: E Estimators; 2.6 Selected References; THREE Method of Moments, Generalized Method of Moments, and Estimating Equations

3.1 Introduction3.1.1 A Just-Determined Moment System with Random Sampling of Scalars; 3.2 Just-Determined Moment Systems, Random Sampling, and Method of Moments (MOM); 3.2.1 General Asymptotic Properties; 3.2.2 Linear Model Semiparametric Estimation through Moment Equations; 3.2.3 MOM Conclusions; 3.3 Generalized Method of Moments (GMM); 3.3.1 GMM Framework; 3.3.2 GMM Linear Model Estimation; 3.3.2a Optimal GMM Weight Matrix; 3.3.2b Sampling Properties of Estimated Optimal GMM (EOGMM) Estimator; 3.3.2c Hypothesis Testing and Confidence Regions

3.3.2d Additional Properties of the GMM Approach3.3.2e Summary and Forward: The GMM Approach; 3.4 Estimating Equations; 3.4.1 Duality between Estimating Equations (EEs) and E Estimators; 3.4.2 Linear Estimating Functions (EFs); 3.4.3 Optimal Unbiased EFs; 3.4.3a Unbiasedness; 3.4.3b Optimal Estimating Functions (OptEFs): The Scalar Case; 3.4.3c OptEFs: The Multivariate Case; 3.4.4 Inference in the Context of EE Estimation; 3.4.4a Wald (W) and Z Tests and Confidence Regions; 3.4.4b Generalized Score (Lagrange Multiplier-Type) Tests and Confidence Regions

3.4.4.c Pseudo-Likelihood Ratio Tests and Confidence Regions

Notes:

Title from publisher's bibliographic system (viewed on 05 Oct 2015).

Includes bibliographical references and index.

ISBN:

1-107-22582-5

1-280-56869-0

1-139-22227-9

9786613598295

1-139-03384-0

1-139-22398-4

1-139-21746-1

1-139-21438-1

1-139-22055-1

OCLC:

775869839