Methods For Estimation And Inference In Modern Econometrics
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Methods for Estimation and Inference in Modern Econometrics
This book covers important topics in econometrics. It discusses methods for efficient estimation in models defined by unconditional and conditional moment restrictions, inference in misspecified models, generalized empirical likelihood estimators, and alternative asymptotic approximations. The first chapter provides a general overview of established nonparametric and parametric approaches to estimation and conventional frameworks for statistical inference. The next several chapters focus on the estimation of models based on moment restrictions implied by economic theory. The final chapters cover nonconventional asymptotic tools that lead to improved finite-sample inference.
Econometric Modeling and Inference
Author: Jean-Pierre Florens
language: en
Publisher: Cambridge University Press
Release Date: 2007-07-02
The aim of this book is to present the main statistical tools of econometrics. It covers almost all modern econometric methodology and unifies the approach by using a small number of estimation techniques, many from generalized method of moments (GMM) estimation. The work is in four parts: Part I sets forth statistical methods, Part II covers regression models, Part III investigates dynamic models, and Part IV synthesizes a set of problems that are specific models in structural econometrics, namely identification and overidentification, simultaneity, and unobservability. Many theoretical examples illustrate the discussion and can be treated as application exercises.
Estimation, Inference and Specification Analysis
Author: Halbert White
language: en
Publisher: Cambridge University Press
Release Date: 1996-06-28
This book examines the consequences of misspecifications for the interpretation of likelihood-based methods of statistical estimation and interference. The analysis concludes with an examination of methods by which the possibility of misspecification can be empirically investigated.