Overview

For graduate-level courses in Introduction to Econometrics. A standard text/reference in courses that include basic techniques in regression analysis and extensions used when linear models prove inadequate or inappropriate. Areas of application include Economics, Sociology, Political Science, Medical Research, Transport Research, and Environmental Economics. This book introduces students to the broad field of applied econometrics. An effective bridge to both on-the-job problems and to the professional literature, it features extensive applications and presents sufficient theoretical background to enable students to recognise new variants of the models that they learn about here as merely natural extensions that fit within a common body of principles.

Full Product Details

Author:   William H. Greene
Publisher:   Pearson Education (US)
Imprint:   Prentice Hall
Edition:   6th edition
Dimensions:   Width: 23.60cm , Height: 4.60cm , Length: 19.90cm
Weight:   2.026kg
ISBN:  

9780135132456

ISBN 10:   0135132452
Pages:   1152
Publication Date:   03 September 2007
Audience:   Professional and scholarly ,  Professional & Vocational
Replaced By:   9780131395381
Format:   Hardback
Publisher's Status:   Out of Print
Availability:   Out of stock  

Table of Contents

Preface Chapter 1 - Introduction Chapter 2 - The Classical Multiple Linear Regression Model Chapter 3 - Least Squares Chapter 4 - Statistical Properties of the Least Squares Estimator Chapter 5 - Inference and Prediction Chapter 6 - Functional Form and Structural Change Chapter 7 - Specification Analysis and Model Selection Chapter 8 - Generalized Regression Model and Heteroscedasticity Chapter 9 - Models for Panel Data Chapter 10 -Systems of Regression Equations Chapter 11 - Nonlinear Regression Models Chapter 12 - Instrumental Variables Estimation Chapter 13 - Simultaneous-Equations Model Chapter 14 - Estimation Frameworks in Econometrics Chapter 15 - Minimum Distance Estimation and the Generalized Method of Moments Chapter 16 - Maximum Likelihood Estimation Chapter 17 - Simulation Based Estimation and Inference Chapter 18 - Bayesian Estimation and Inference Chapter 19 - Serial Correlation Chapter 20 - Models With Lagged Variables Chapter 21 - Time-Series Models Chapter 22 - Nonstationary Data Chapter 23 - Models for Discrete Choice Chapter 24 - Truncation, Censoring and Sample Selection Chapter 25 - Models for Event Counts and Duration Appendix A: Matrix Algebra Appendix B: Probability and Distribution Theory Appendix C: Estimation and Inference Appendix D: Large Sample Distribution Theory Appendix E: Computation and Optimization Appendix F: Data Sets Used in Applications Appendix G: Statistical Tables References Author Index Subject Index

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